Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Training Strategy of Music Expression in Piano Teaching and Performance by Intelligent Multimedia Technology

Training Strategy of Music Expression in Piano Teaching and Performance by Intelligent Multimedia... Hindawi International Transactions on Electrical Energy Systems Volume 2022, Article ID 7266492, 14 pages https://doi.org/10.1155/2022/7266492 Research Article Training Strategy of Music Expression in Piano Teaching and Performance by Intelligent Multimedia Technology YunDan Zheng , Tian Tian, and Ai Zhang Academy of Arts, Chongqing College of Humanities, Science & Technology, Hechuan, Chongqing 401524, China Correspondence should be addressed to YunDan Zheng; noreen_bradford@stu.centralaz.edu Received 17 June 2022; Revised 12 July 2022; Accepted 18 July 2022; Published 28 August 2022 Academic Editor: Raghavan Dhanasekaran Copyright © 2022 YunDan Zheng et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. st Teaching using a multimedia technology in the 21 century affords the possibility of developing novel instructional strategies and paves the way for the all-around extension of musical educational functions. *e importance of multimedia teaching technology in piano instruction has started to emerge in our country and society due to the ongoing development of this kind of technology in music educational institutions here. *e conventional method of teaching piano has several drawbacks that may be mitigated by using one of the several alternative methods of instruction for the instrument, especially in light of the ongoing advancements in science and technology. A pianist’s methods of expression are the tools they use to convey their thoughts and emotions about a piece of music to the audience. Teachers may demonstrate their musical skills to students and they must immediately focus on a musical expression which is vital for performers. In this paper, the Multimedia-based Piano Teaching Model (MPTM) has been proposed to improve the piano teaching quality. Traditional piano instruction is improved and developed using multimedia technology in this article. *e Internet education model is used for teacher assessment, and the systematic way representing piano teaching combines different music educational materials. It begins building a sufficiently broad music network infrastructure resource sharing framework and benefits society’s amateur music literacy. *e use of machine learning in students’ concrete piano instruction has the potential to thoroughly promote contemporary piano instruction and enhance the overall quality of in- struction. To begin, an explanation of the intelligent piano’s features and capabilities is provided. *e neural network is used to suggest a technique for detecting a piano note on a set. *e network can assess the input piano music signal’s time frequency by translating the original time-domain waveform into the time-varying frequency distribution. Intelligent piano instruction analysis can effectively achieve the overall optimization of piano performance. *e test results show that MPTM has a significant role in boosting the desire to learn to play the instrument. *e experimental results show that the proposed MPTM achieves a learning skills ratio of 97.6%, a learning activity ratio of 98.5%, a student performance ratio of 93.8%, a teaching evaluation ratio of 90.3%, and a learning behavior ratio of 94.2% when compared to other methods. pianos may be used to educate online students on how to 1. Overview of Multimedia-Based Piano play the piano, gradually allowing them to realize its in- Teaching Model (MPTM) telligence [4]. As a result of inefficient and chaotic management *e demand for piano instruction is increasing, and the practices, the growth of piano teaching activities is severely number of piano instructors is increasing [1]. People’s eyes hampered by the conventional manual techniques used in are being opened to a new generation of multimedia piano traditional piano education management [5]. Lessons in the instruction resources like technology and the Internet, traditional “teacher with piano content” method fall short of which continue to advance [2]. Multimedia piano instruc- student expectations. Developing piano instruction will be tion is widely disseminated through the Internet in several more effective if multimedia technology is used [6]. Learners methods. *is rich and diversified teaching method is be- are provided less time to play the piano and cannot obtain coming more popular [3]. Internet-connected intelligent 2 International Transactions on Electrical Energy Systems (ii) Piano instructors and students worked together to sufficient practice, making it difficult for students to apply the information they have gained from their professors to develop an assessment index for the neural net- works used in this model. actual piano playing [7]. Teachers’ energy and time are limited and because they cannot identify the challenges that (iii) *e NN’s training has been completed and the every student faces, it is difficult for a teacher to offer a lesson piano teaching procedure has been verified using that is individualized for each student’s level of proficiency piano performance data. [8]. In addition, because students in big classes have varying *e overall organization is as follows: Section 1 discusses levels of competence, learning process of pupils is made the introduction of piano teaching, Section 2 deliberates the more difficult by the teaching environment [9]. related works, Section 3 explores the proposed MPTM with *e use of multimedia technology in piano instruction at machine learning techniques, Section 4 explores the results universities and colleges represents a significant revolution in and discussion, and Section 5 demonstrates the conclusion music teaching and educational method in colleges and of the paper. universities, indicating that music education has entered a new age [10]. *e use of multimedia technology in college and university piano instruction helps change abstraction into 2. Related Works concreteness, which is important for boosting students’ ca- pacity to enjoy music [11]. *e use of multimedia technology JAVA-based Piano Teaching Management System (JAVA- in piano instruction accomplishes the union of technology and PTMS) for analysis of the status quo and problems of piano science which can realize all-dimensional high-efficiency ed- teaching informatization was described by Nie [18]. By ucation [12]. It helps increase the piano’s attractiveness to analyzing the current reasonably mature technological pupils which is important for increasing students’ interest [13]. framework and programming language, this study chose to Machine learning is used to analyze multimedia piano implement a B/S system architecture and an SSH framework instruction performance information which offers decision for the major body of the system. JAVA was used to build assistance for teaching managers and is most important for and construct the system based on the structural design idea. improving multimedia piano teacher performance [14]. In *ere has been a successful implementation of the finished the functional design of the piano teaching operating system, piano instruction administration system. As a result, the neural network (NN) music visualizations are a crucial school’s limited piano teaching resources cannot fulfil the contribution [15]. Students can watch their piano perfor- growing demand from students with such inadequate piano mances, allowing them to fully comprehend the information foundations, distinct understandings, and preferences for in the song they play. Machine learning techniques and NN the piano. *e findings of the experiments reveal that a more representation learning are widely utilized in the age of stable system has a quicker reaction time. information processing jobs [16]. For advising the student in Digital Piano Training System based on Technological playing practice, a music assessment system based on the Pedagogical Content Knowledge (TPACK) for analyzing NN model is determined [17]. preschool students’ piano performance was discussed by Students’ capacity to enjoy music may be improved via Changhan et al. [19]. 30 of the university’s preschool multimedia technologies in college and university piano students were randomly chosen and offered a one-month instruction. MPTM emphasizes practical and deliberate trial of the Digital Piano Training System (DPTS) tech- difficulties that may not occur in smaller classes. A range of nology at a public institution in Northeast China, which supervised, unsupervised, and semisupervised machine provides digital piano lessons and has 360 students en- learning algorithms have attracted much interest in this rolled. *erefore, it was established and validated that the domain. *is study proposes a specific machine learning DPTS has been the most effective piano teaching instru- approach for evaluating the possible association of piano ment for preschool pupils. As a consequence, institutions instruction. *is study examined two major components of should consider using DPTS as one of their piano teaching piano music learning. Machine learning methods may im- methods. prove piano music courses for various learning styles and Blended Piano Teaching Model (BPTM) for students at audiences. *e automatic creation of lesson plans that may the University of Hunan City who are not majoring in instruct music fans to play their favourite instruments Filipino music should take this course deliberated by Zhu provides access to distinct learning styles, diverse musical [20]. Regarding sight-reading, scales and arpeggios, etudes backgrounds, and talents. Machine learning is used in music and piano pieces, the experimental group utilizing the automated recording technology to determine the imple- BPTM model outperformed the control group statistically. It mentation principle and legislation of a piano automatic was inferred and proven that the BPTM was an effective recording system. Music, rhythm, and instruction may all teaching instrument in piano instruction for nonpiano benefit from the integration of piano music technology, majors. which is the focus of this course. Piano Teaching Strategy (PTS) for teaching organic concepts was expressed by Yonathan [21]. In-depth inter- views with students and participant observation were the 1.1. !e Main Contribution of the Study main modalities of data gathering. Setting objectives, (i) *is study presents an evaluation technique based modelling, listening, visualization, breakdown of the musical on the NN model for students’ playing practice. structure, and subdivision assistance are among Arens’ International Transactions on Electrical Energy Systems 3 teaching tactics. Arens teaches that procedures and creative performance, complicated networks and multimedia tech- interpretation are the same rather than taught separately nology have been studied extensively. *is article aims to which is a huge difference. identify, examine, investigate, and assess current piano *e Internet with Piano Intelligent Network Teaching training techniques to maintain only those that comply with System Model (IPINTSM) for using Internet technology was contemporary theories of learning, educational standards, explored by Shuo [22]. *e acoustic and multinote models of and the distinctive qualities of the piano discipline. Before the HMM with many tones were created with the aid of the introducing various network training instances for piano Internet. An IPINTSM is created after determining the instructors to use and investigate, the pros and disadvan- match between the testing audio and the multinote models. tages are considered. Switching to a more student-centred *e most accurate multinote identification rate was found teaching style will improve the initial single-class teaching when the multistate recognition system included seven method. *e innovative teaching approach allows students states. When the IPINTSM multinote recognition state is 7, to learn the piano at their speed via multimedia and network the IPINTSM has generated fresh inspiration that is more technology. In this study, the course material is digitized and accurate. represented using machine learning algorithms, and the Students’ perspectives on distance piano education were systems that allow access and availability of the produced qualitatively analyzed by Unlu¨ [23]. *e research explores content are maintained using the NN and machine learning how university students who get piano instruction through technologies with or without Internet assistance. When distance education are influenced favorably or adversely. these two technologies are used with piano instruction, Qualitative research was conducted utilizing the phenom- higher music education on machine learning may be more enological design technique to achieve the results. *e effective. study’s findings indicated that students did not find piano Offline instruction is face-to-face piano teaching in a lessons through distance learning advantageous in general smart classroom shown in Figure 1. Online teaching com- and that students’ overall performance suffered as a plements offline teaching using microlecture videos, distant consequence. education, and smart partner training technologies. Eye- Microlecture Flipped Classroom Piano Teaching Model strain is likely to develop when there is a significant contrast (MFCPTM) for analysis of piano teaching mood was ini- in brightness between the musical notation and the sur- tialized by Fu [24]. *is essay delves deeply into the theory of roundings, such as when the instrumental music is darker the microlecture flipped classroom and explores the benefits and the environments are light (similar to using a smart- of incorporating it into the practice of piano instruction. *e phone in the dark). A piano light such as the LED piano light excellent method of MFCPTM to optimize piano teaching in is required to correctly illuminate the sheet music and the the microclass flipped classroom can benefit colleges and piano keys. Light Emitted Diode (LED) indications in the institutions and provide a useful reference for strengthening piano game mode motivate kids to practice. Students may the piano teaching in the micro-class flipped classroom in study music in a pleasant and joyful setting, increasing their colleges and universities. sensitivity to music and enthusiasm for learning and making Linna et al. [25] expressed the Wireless Network in Piano piano instruction dull. With the introduction of piano Music Teaching based on artificial intelligence. Artificial teaching apps, the intelligent piano may be linked to the app intelligence (AI) advancement offers a new path for the old software through smart devices to enable human-piano educational approach. In the opinion of piano majors, a interaction. *e Garage Band application program may smart piano benefits novices and those with limited piano create music without requiring professional Musical In- skills. On the other hand, high-level students will not benefit strument Digital Interface (MIDI) equipment, giving music from smart pianos. Teachers should use various techniques enthusiasts more options. Synthesizers, samplers, and to meet students’ needs at various skill levels in the class- computers all can interact with one another via the use of room. Finally, based on the existing state of the smart piano, MIDI signals. MIDI is a method for connecting devices that countermeasures and proposals for the future development generate and control music. *e MIDI controller or key- of the smart piano in piano music instruction are proposed. board can also imitate a violin, flute, bagpipe, or any other Based on the analysis of existing methods, piano instrument for which samples can be found. Familiarity with teaching needs to be improved more effectively. *e pro- the controls will have a pleasant manner of playing them into posed method, MPTM, utilizes the neural network to an- the music. Built-in systems of intelligent pianos and network alyze the student’s performance and predicts the teaching piano classrooms provide recorded or live video instruction quality to deliver the desired outcome. courses. Teachers and students may communicate through network voice and video. Teachers and students interact via example, dialogue, and visuals. 3. Multimedia-Based Piano Teaching Smart pianos give scores to students based on their pitch, Model (MPTM) rhythm, and strength. Each student understands their learning circumstances and inadequacies. Student assess- With the advent of multimedia network teaching as a ment is three-staged. First, first-time intelligent piano users contemporary teaching mode, new educational techniques must examine their piano-learning level, interest, and future may be developed and educational functions can be ex- aspirations. Students without a piano basis should start with panded in all directions. With piano education and 4 International Transactions on Electrical Energy Systems Piano performance and teaching evaluation Music appreciation Online and offline teaching Distance Offline teaching Online teaching of intelligent piano learning Solfeggio Intelligent Music reading Musical Micro lesson sparring preview teaching composition Figure 1: Hybrid teaching of o“ine and online piano teaching model. the basics, while those with a foundation may pick the test’s E(y) ly, di culty level. Second, the repertoire’s song, rhythm, and power are rated. ird, students’ results determine a learning E(y) σ, k, wheny ≥ θ, (2a) plan for their present level. Smart piano assessment helps instructors train students according to their potential. El- E(y) σy, wheny − θ < y < θ, ementary learners benet from one-to-many group edu- cation, whereas high-level learners prefer one-on-one. In E(y)−σ, k, whenyy ≤ θ, each step of student learning, sensible assessments of their (2b) E(y) σ, wheny ≥ θ, learning circumstances may drive learning passion via group competition. e smart assessment module may convey E(y) ϕ, when y ≥ θ, (2c) kids’ music and class successes to parents through the network so they can monitor their students’ development. Students may also self-evaluate and evaluate based on in- E(y) α + . (2d) structor feedback. Teachers may master students’ learning 1 + exp(−dy) and make modications and innovations based on their As discussed in equations (2a), (2b), (2c), and (2d), suggestions. transfer functions have been deliberated. Equation (2a) Simulating real neurons is a complex task that takes denotes the linear function, (2b) shows the nonlinear slope more than mere easiness; it demands an understanding of function, (2c) shows the step function, and (2d) represents the fundamental properties of biological neurons. As with the t-type function, where σ is a transfer function variable, real neurons, the “connection strength” of an articial θ is an angle, a is a transfer function variable, ϕ is a step neuron may be determined by the weighted total of the input function coe cient, and l is a slope function variable. A signals from other neural connections, each of which rep- network’s eˆectiveness might be severely aˆected by resents a possible output, and this sum denes the neuron’s transfer functions that exponentially increase network active state. e Z ,Z , ... ,Z coupling weighting matrices 1 2 m input; hence, the selection of transfer functions for various correlate to the y ,y , ... ,y feature vectors, which com- 1 2 m application areas of the NN model is crucial. A lot of people prise the M inputs. Neurons are depicted by input and are using the new feature. In general, the hidden layer connection vectors Z and Y, respectively, which show the employs a t-shaped function, and the output layer utilizes a cumulative inƒuence of the input signal on the neuron’s linear one. output. Let us assume that the neural network has m nodes in the input layer, that the hidden layer has p nodes, that the output snet y z . (1) j j layer has m nodes, and that there is a weighted correlation j1 between the weights of U and Z between the input and jl jl As found in equation (1) neuron input signal has been hidden layers. Its transfer functions are E (y) and E (y), 1 2 and the output of the node that is on the buried layer is described. After receiving input from the network neurons represented in E (y). should provide the desired results. Whenever the cumulative eˆect of its input signals hits this threshold, each neuron is in an excitation state. In this suppression state, the neuron does     W  E  U y . (3) l 1 jl j not respond to the input signal. E(y): out Enet represents j0 the transfer function for articial neurons, and R the output As shown in equation (3) hidden layer node has been of neurons. Linear, nonlinear ramp, step, and t-type transfer demonstrated. e output layer node’s outcome is repre- functions are all examples of typical governing equations, as sented by the following expression: shown in the following equations: International Transactions on Electrical Energy Systems 5 m Furthermore, we found that CiP may help the instructor ⎛ ⎝ ⎞ ⎠ x � E 􏽘 Z W . execute even an unprepared piece to their delight. As a (4) j 2 jl l j�0 result, they are free to focus their full attention on musically expressing themselves. *is output information is being In equation (4), output layer node outcome is calculated for recorded by the piano that is attached. Before beginning to teaching evaluation ratio, as well as student performance. *e play it is important to input into the computer the sequence rhythm quality is extracted from the music score bar by bar, of pitches that will be used in the composition that will be using each musical section. Each performance segment may be played. *e MIDI note numbers are what are used to specify precisely pinpointed using the score split into music subsec- the pitches. During the performance, the computer will tions. *e current bar’s rhythm and beat may be determined replace the note numbers that were fed into it with the note based on the performance time’s placement. It is the rela- numbers that are being played. tionship between the length of a given note, also known as the At last, the computer will output the note numbers that time point, and the duration of the pronouncing of each note; have been replaced and then feed those numbers into the as shown in the picture, its scoring with four quarters sounds as tone generator. In light of this, the CiP can always output one measure. By isolating the tempo from the current bar, sound at the correct pitch, even if the performer touches the rhythm will be able to evaluate the player’s capacity to keep up wrong keys. At this time, CiP is capable of handling mo- with a moderate beat. Note the times at which the player nophony. CiP will not react to any key-down events within depresses and releases each of the four notations while the first fifty milliseconds after a previous key-down event. maintaining alignment with the first note played at the be- *is is done to account for accidental touches. ginning of each bar. Following notes are dependent on how On the other hand, note-on velocity (when the key is long the preceding note has been spoken, according to the pressed down), note-off velocity (when the key is pressed rhythm’s qualities. As previously stated, subsequent notes up), and pedal messages are generated while the player would have mistakes if the prior note was improperly handled. performs. *e different parts of the instructor’s musical Various weights must be given to different notes to assess the knowledge are represented in the same way the instructor level of rhythm mastery. To determine how well the rhythm of conveys them. *is is the first system developed to learn and this portion of music is understood, divide the standard value predict polyphonic expression in piano music with various by the pronunciation point for each note and multiply the performing styles. Polyhymnia can generate expressive result by the weight of that note. polyphonic piano performances using music scores, so that 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 it may be utilized as a computer-based tool for expressive 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 E(y) � 􏽘 􏼐T − A 􏼑 + 􏽘 P 􏼐F − C 􏼑 P . (5) 􏼌 j j 􏼌 j􏼌 j j 􏼌 j performance. Polyhymnia can completely computerize an expressive performance on the piano. *e fundamental As obtained in equation (5), rhythm music has been structure of an expressive performance may be deduced determined. *is measure’s note sequence number is j. *e from musical symbols, and these symbols themselves can be number of notes in this measure time the player pushes interpreted in several different ways. Polyhymnia is an releases a key t and standard release time is all included in automated piano performance system that can learn and this measure’s notes list; C is the usual release time and P is anticipate polyphonic expressiveness and interpret musical the amount of weight that corresponds to the different notes. symbols mechanically. According to the results of experi- For each measure, the note volume is quantified and ments involving produced performances, the system pro- recorded. In terms of rhythm, the first note of each bar is duced performances with polyphonic expressiveness and critical. *e beat characteristic of this bar may be extracted dynamic sound, and human listeners could discern between using the same procedure and various weights for different the various performance styles. Machine-rendered piano notes as shown by the following: performance might benefit by modelling the hierarchical 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 E(y) � 􏽘 􏼐B − A 􏼑 P . (6) structures of a specific composition. Interactive performance 􏼌 j j 􏼌 j may be achieved by extending Polyhymnia model param- Beat feature function is given in equation (6). *ese eters via an interface. *e use of adaptable parametric variables are note count j, note volume (B), bar length (A), models for their automated interpretation is advocated. weighted value (P), and chord volume (A) with j being the Since piano music is often polyphonic, emotive piano number of notes in each bar. performances must include polyphonic characteristics. *is *e structure of CiP is shown in Figure 2. *e “Coloring- study refers to musical expression with such characteristics in Piano” (CiP) is a novel musical instrument in which as polyphonic expression. *e suggested statistical model- traditional pianos do not require their players to reproduce ling of polyphony piano renditions demonstrated that the melody line meticulously. Using CiP enables them to performance produced with polyphonic expression looked focus directly on the musical expression crucial for the better than presentations without it. *is has been accom- performers, allowing the instructor to demonstrate their plished by demonstrating that polyphonic expression can be musical understanding to the student. *ere are a series of modelled statistically. *e results of experiments conducted trials to compare the performance of Coloring-in Piano with on performances created by Polyhymnia using a variety of that of a regular piano. It is clear from these findings that a compositions suggest that they had polyphonic expres- pianist using CiP can play just as well as one using a tra- siveness and sounded expressive. *e majority of these ditional piano. systems focus on discussing different interpretations of 6 International Transactions on Electrical Energy Systems Inputs by Output by Polyhymnia Student System Melody Piano MIDI Expression Score Estimator Harmonic Basic Deviation Expressive Expression Tempo Generator Performance Estimator MP3 Musical Scaling Symbol MP3 Ratio Interpretor Figure 2: Tune generating. monophonic tunes. Still, polyphonic interpretations have speed at which a note-on message descends a key is almost received less attention owing to the computational com- identical to the note’s volume. plexity and enormous quantity of required data. *e piano’s control flow diagram shows a subtemplate for *ere is little discussion over the automated interpre- control in Figure 3. *ere are three distinct sorts of piano tation of musical symbols based on extremely basic prin- playing: the piano itself, the piano’s white key, and the piano’s ciples, since they enter a MIDI-like score. A music expressive black key. *e piano type is responsible for managing all performance may be provided by certain commercial no- aspects of piano performance, including the instrument’s tation software. It is not known how they likely come up with startup, playing, and sound. *e piano white key and the musical expressions that they use basic criteria to under- piano black key may be used as a single white or black piano stand musical signals. Polyhymnia users must enter a piano key, respectively. *e piano-playing module, the pianist audio score in MusicXML format to get an expression piano processing module, and the piano screen and controller make performance. MusicXML, unlike MIDI, can digitally encode up the piano teaching system. Figure 3 depicts how the almost all musical symbols. A mathematical model is used to system’s piano display and control module completes its tasks. analyze encoded musical signals and create a variety of *e process begins with the activation of the virtual piano and possible interpretations for each symbol. Conditional then moves on to the initialization of the piano’s variables, the Random Fields (CRFs) are used to teach and produce showcase of the piano interaction, and finally the tracking of polyphonic expressiveness for polyphonic piano renditions. the piano audio input operations. *e piano interface will be Scaling ratios influence the expressiveness of the resulting redisplayed once the operation is processed and detected music. In addition to MIDI and MP3 files, the system enables while using the piano interface. expressive performances. Following the activation and initialization of the ana- To determine gap value using the performance data in logue piano, the piano disk interface will be shown and Musical Instrument Digital Interface (MIDI) format, the playing will be observed. After the detection of playing interonset interval (IOI) may be calculated as follows: actions variety of playing events will be handled to bring IoI � S − S . (7) about an update to the piano’s user interface that will show j non(j+1) non(j) the final effects. *e piano audio processor module is re- As described in equation (7), IOI has been deliberated, sponsible for simulating the sound of a piano and improving where IoI refers to the j-th information output indicator, j such a sound. Applying certain treatments to the sound will S refers to the emitted time of the j-th note-on message non(j) become richer and more pleasant, significantly increasing S , and S refers to the emitted time of the non(j) non(j+1) the auditory experience that the instructor and the students (j + 1)-th note-on message non(j + 1). *e value of the gap have. *e addition of a module that generates a soft sound may be found by doing the following: source will contribute to the production of a better voice effect. gap � S − S . (8) j non(j+1) noff (j) An observation of understanding has been made and is As found in equation (8) gap value has been identified, particularly true when compared to an investigator to aid where S is the note-off message’s emission time and comprehension. A central location provides a great position; noff (j) S is the (j + 1) note-on message’s emission time a disadvantageous location offers an important comparison non(j+1) having gap � S − S . As a result, if the j-th note point. After learning about scribe searches learner adopts a j non(j+1) noff (j) has a positive gap performer has shortened it. In addition, new point of view on the world and records that perspective the MIDI note-on message’s velocity data are retrieved. *e in the dataset sin(c) � n /n and sin(β) � n /n . 1 j 2 j International Transactions on Electrical Energy Systems 7 Start the virtual piano Initialize the piano parameters Listening Piano zoom, Display piano Piano zoom, pan translation interface control interface operation Yes operation processing No Figure 3: Piano control ƒow chart for controlling subtemplates. Remove g from equation (9) to get the most basic As deliberated in equation (11) key function has been knowledge of the item in the central library. demonstrated. When deciding how to implement remote music education, the goal is to teach participants in an n n 1 2 g   sin(c) +  sin(c) . j organized and simplied manner to integrate knowledgeable (9) n n j j j1 j1 predened attitudes and morality adapted for specic thoughts and actions. is goal n V + n V /nv is achieved 1 1 2 2 As demonstrated in equation (9) central library has been by machine learning teaching techniques. described. A survey questionnaire, developed, integrated, and scored after the discovery of the teaching materials, will n V + n V 1 1 2 2 Learning Behavior   n V , j j investigate the impact of distant location music piano ed- n V j j (12) ucation on machine learning and networked instructional methods on the unique educational capabilities of learners. n V + n V . 1 1 2 2 Objectivity, e ciency, and quantiable n /V analysis are all j j advantages of this approach. It is a common way to gauge a As initialized in equation (12), learning behavior has person’s capacity for self-directed learning, as an alternative been calculated. is belief is shared by educators who feel to specic learning activities, such as that learning  n V is more than simply a means to an j j end. Learning activity   n V sin(c). (10) j j j1 n V + n V 1 1 2 2 Learning skills Rate n V + . (13) j j As obtained in equation (10) learning activity has been n V j j j discussed. Piano music learning sin(θ) is a way of teaching n(c − σ) which includes an educational system to recognize As expressed in equation (13) learning skills rate has the unjustied progression of sin c to σ such a new economy been formulated. Learning skills |N(m)| such as notoriety, that has nally lost its key function in continuous learning as recognition, the capacity to grasp, and encouragement may proven by be used by students to increase their skill set. sin(θ)  sin(c − σ) |N(m)|   observing|n(N(m))| + n V . (14) j j j j sin c sin σ + tan c tan σ (11) As shown in equation (14), to achieve the primary goal of n V n V n V + n V 1 1 2 2 1 1 2 2 piano music education based on machine learning and 5G +  . n V n V nv network expansion, a socialist and optimization strategy j j j j 8 International Transactions on Electrical Energy Systems communications technology for data processing and the must be underscored to break free of organizational limi- tations and identify an effective structure for the delivery of transmission of information. With machine learning, a method in which the processing unit is supplied with the the indispensable university degree. See Figure 4 for multimedia intelligent piano teaching network edges, the efficiency of the computer processing system. Here, we provide a system for intelligently teaching may be improved. *e information must first be sorted and piano using the multimedia with teaching system, which evaluated to determine how the data are distributed. Mul- includes a system for teachers, one for students, and one for timedia-assisted piano teaching and classroom management back-end administration (Figure 4). To assist students in can be improved by using data characteristics, rules, and improving their musical knowledge and their ability to play connection structure at the edges. According to the infor- mation provided by the evaluation feedback, it is necessary chords, piano teachers use clever methods of instruction. Instructors must devise instructional strategies that use to find timely solutions to the various issues that have arisen in the teaching, further optimize the different aspects of students’ strong sense of wonder, curiosity, and inventive- ness to keep their students engaged and motivated to learn. multimedia-assisted piano teaching and give full play to the obvious benefits of multi-coal-assisted piano teaching, and Teachers may play an imported animation of piano lessons on the smart piano classroom’s screen using the system’s establish the underpinnings for increasing the quality of teaching feature as a second option. As a result, students can piano instruction. Information literacy for educators and the make notations on a smart music score shown through promotion of educators’ personal growth and development multimedia by their instructor. *e instructor shows how to by teaching the piano with the assistance of multimedia play a chord and teaches the proper technique. A standard combine the benefits of both conventional teaching and performance video is also entered into the system so stu- multimedia teaching and play a significant role in enhancing the quality of instruction. However, at the same time, more dents can view it repeatedly after class. In addition, teachers must show and explain the fingering animation video and stringent standards should be put up about the qualifications and skills of instructors participating in multimedia piano the associated knowledge points. *e instructor checks whether each child’s hand form and chord are correct instruction. With the assistance of edge computing priority to offer educators helpful information throughout evaluat- throughout the playing process and encourages fingering. Sensible piano instruction may improve students’ passion ing students, this may be done via the evaluation system’s for piano practice and their capacity to study independently. orientation, diagnosis, and quick feedback. Analyzing as- Using their accounts in the intelligent piano application, sessment data, it is possible to identify instructors’ obstacles students may log in and practice at their own pace under the and inadequacies in multimedia piano education. To assist guidance of their parents in the intelligent piano class. teachers in enhancing their knowledge of multimedia in- By employing data acquired via research, an evaluation formation, emerging technology, and capacity for classroom integration to foster both the self-improvement and im- aims to enhance scientific methods and researcher decision- making. Edge-commuting can enhance this assessment. proved performance of piano players, Figure 5 displays the assessment index for the multimedia-assisted piano in- Data analysis will be more efficient as a result of the edge- enabled approach. *e assessment information facts do not struction approach. come into action by themselves. Proper processing of as- *e use of multimedia education as an additional tool for sessment data and feedback is required. *e feedback as- piano instruction has revolutionized and reshaped the way sessment information plays a vital part in making the students learn the fundamentals of piano instruction. It may evaluation process successful and feasible. *e development help students learn the fundamentals of piano theory and of multimedia-assisted piano education is a complicated and play more quickly and efficiently. An autonomous piano organized undertaking strongly tied to the relevant theo- performance system should handle various unidentified retical research, technical advancement, and teaching piano compositions. *ere was evidence of polyphonic expressiveness and expressive sound in the performances practice. Many factors influence its usefulness as a teaching tool, including the quantity and quality of information lit- made by Polyhymnia using diverse compositions. A piano piece may be played in a variety of different ways. It is eracy teachers and students, as well as access to teaching support platforms. *e MPTM team is now in the process of possible to create a wide range of models by practicing determining the best ways to teach students. various performance styles. Polyhymnia’s experimental When designing multimedia-assisted piano instruction, findings on various performances show that each trained employing massive machine learning technologies to study model reflects the performance style in the training set. the factors that influence the effectiveness is a good place to Taking piano solo lessons may be arranged to be one-on-one start. *e acquisition of useful knowledge is of the utmost or in a group setting to integrate smart piano technology with more conventional piano instruction methods. As an importance to enhance the standard of multimedia in- struction, with responses to questions concerning the experiment, a teacher and an intelligent piano may work together to collect data for six weeks to evaluate how well the evaluation, analysis, and prompt alteration of instructional methods. In today’s information society, the swiftness with class is progressing and the overall quality of the material taught. Lessons on the intelligent piano will be taught in which feedback can be provided and the efficacy with which information can be transmitted both speed up the process of groups during the first and second weeks, lessons on the social evolution. During the assessment process, it is im- intelligent piano will be taught to students individually portant to factor in the potential benefits of adopting data during the third and fourth weeks, and lessons on the International Transactions on Electrical Energy Systems 9 Teacher account number Attend class and lesson preparation Intelligent piano Teacher Back-stage teaching system Module appreciation Student account and class organization number Effect of piano practice Class account number Students Basic teaching Composition and solfeggio Playing the piano and understanding music Figure 4: Piano display control subtemplate. Evaluation Evaluation IndicatorData Indicator Core Processing Data Processing Piano Pre-image Result Check Result Teaching Library Library Library Piano Storage Business Metabase Teaching Upload Synchronization Basic Check Rule Multimedia Result Feedback Library Service Library Management Multimedia Statistical Business Piano Teaching Indicator Information Basic Library Base Information Identity Check Verification Historical Information Access Service Area Figure 5: MPTM-based teaching evaluation. 10 International Transactions on Electrical Energy Systems traditional piano will be taught to students individually chord, whether it is sharp or flat, whether it is major or during the fifth and sixth weeks. *e foundation is the minor, and its location on the keyboard may be found in the Chord column of the table. *e additional column labelled regular stepwise and complementing instruction throughout the teaching process. *e benefits of this new unified “Note” contains the individual notes that make up the triad, teaching approach will be reflected in the final evaluation of arranged in increasing order (https://www.kaggle.com/ students’ degrees of completion, which will be determined datasets/davidbroberts/piano-triads-wavset). by combining the conclusions formed from the feedback *e number of students is on the x-axis and y-axis in- from students weekly. Figure 6 depicts the intelligent piano cludes learning activity, learning behavior, learning skills, lesson encounter process in which the students participate student performance, and teaching evaluation analysis based during the specific trial. on comparative analysis of JAVA-PTMS [18], BPTM [20], *e performances of various piano compositions with PTS [21], and IPINTSM [22] with our proposed method MPTM. different trained models showed polyphonic expressiveness and sounded expressive, according to experimental data. It was shown that human listeners could tell the difference 4.1.1. Learning Skills Analysis (%). Students who get their between the models trained in different performance styles piano instruction through multimedia have more oppor- and the models taught in the same style. tunities to practice independently while following the teacher’s explanations, enhancing their learning efficiency. 4. Numerical Analysis Learning to play the piano aids in the development of previously undeveloped neural connections between the Analysis of the real dataset demonstrates that the suggested hands and the brain. Playing this instrument requires strong, approach is feasible and effective. According to our findings, flexible hands that can perform of their own will. Studying to the potential usefulness of multimedia-aided piano in- play the first scale or chord on the piano is a crucial part of struction assessment data is by employing the degree of learning music theory. With one-on-one teaching, a teacher impact as the measure of association rules. According to the has the luxury of extra time to construct a lesson plan findings, it is out of the question to utilize the degree of specific to each student’s needs and abilities at the piano. impact as an association rule metric to uncover the potential Teachers may adjust their lesson plans depending on the use of multimedia piano instruction assessment data. Mu- progress and results of their students in real time. Figure 7 sicians now have a computer-based tool for crafting ex- shows the learning skills ratio (%) based on the dataset. pressive, polyphonic piano performances to Polyhymnia, As expressed in equation (13) learning skills rate has our automated piano performance system. Piano music may been formulated. *e advancement of the educators’ music be created using digital symbol translations and simulation playing skills will, in turn, enhance their capacity to rec- approaches of intellectual framework that account for ognize elegance, leading to a virtuous cycle that will en- polyphonic qualities. According to experiments, perfor- courage the students’ piano learning. *e improved mances of different piano compositions with various performance of the educators’ ability to enjoy beauty can training images showed polyphonic expressiveness and drive the learners to build their playing skills. sounded expressive. To make things more interesting, the models were trained in different performance styles and 4.1.2. Learning Activity Ratio (%). Individual instruction is were well distinguished by trained listeners. widespread in the activities of college piano teaching. Most individual teachings need the students and the instructors to 4.1. Dataset 1 Description. When developing a Digital Signal finish the instruction in a generally closed-off setting. Re- Processing (DSP) project for a Virtual Studio Technology nouncing the space limitations of the traditional teaching (VST) plug-in, we realized the advantages of digital tech- model teaching activities and multimedia technology can nology in music teaching. *e plan was to employ AI/ML to help students understand the piano theory and practice ability. *is is helpful for students to practice pertinence develop a product. 432 Wav files of piano triads over six octaves are included in this collection. A wave file represents during the practice. All-dimensional audio-video impact may be used to increase students’ emotional and knowledge the 12 major, minor, and diminished triad chords in its root and first inversion. 32 bits, 44 kHz mono, about 520 K size, experiences in the music classroom. Teaching in a more and 3-second duration are the sample formats. Around interactive approach like this necessitates that instructors 200 MB is the overall size of the wave files. Trios.csv is the begin with the facts and look forward to exploring new ideas. CSV file containing the chords’ names, octaves, and in- *e use of multimedia technology does not make it simpler versions. *e notes that make up each chord are also in- for students to locate a wide variety of high-quality resources cluded. Two underscore characters separate the chord names and educational materials about the piano enhance the into four sections. Lowercase “s” indicates sharp notes and students’ capacity for independent study. Figure 8 elaborates chords in musical notation. To denote flat notes or chords, b on the dataset’s learning activity ratio (%) [26]. As obtained in equation (10) learning activity has been is used in lower case. *is document contains a list of chord placements and discussed. *is paper presents the fundamental methodol- ogy of conventional piano instruction and investigates the chords. It is organized into four columns: Chord, Note1, Note2, and Note3. A string that includes the name of the use of multimedia technologies in the context of piano International Transactions on Electrical Energy Systems 11 Carefully read the requirements of the intelligent network piano class and clarify the purpose of the experiment Practice piano playing Use the system boot Wearable system interface to enter the according to the equipment course requirements virtual space scene Take off system equipment End of piano lesson Figure 6: Student piano lesson for student learning performance analysis. 10 20 30 40 50 10 20 30 40 50 Number of students Number of Students JAVA-PTMS IPINTSM JAVA-PTMS IPINTSM BPTM MPTM BPTM MPTM PTS PTS Figure 8: Learning activity ratio (%). Figure 7: Learning skills analysis ratio (%). instruction. is article presents a design for integrating the student modelling module to gather historical data on multimedia technologies into piano instruction. In con- their music listening habits. Piano music education ideals clusion, the research discusses the advancements that have and nonutilitarian music values inƒuence people’s behavior. been made in the eld of multimedia piano instruction and Students’ nonverbal cues are usually useful to piano in- expresses the hope that circumstances may be established to structors in gauging their level of commitment. e length of facilitate the seamless integration of multimedia technology time students spend engaged in a task, the quality of the and the activities involved in piano instruction. work they produce, and the way they communicate their feelings and ideas are all behavioral indications of motiva- tion. Figure 9 depicts the dataset’s learning behavior analysis 4.1.3. Learning Behavior Analysis (%). Neural networks are ratio (%) based on dataset [26]. widely used in piano playing and education. A neural As initialized in equation (12), learning behavior has network-based recommendation model is the focus of this been calculated. Using the preprocessed performance ma- study, which focuses on piano performance and training terial and spectral properties extracted from the spectrum by systems that are based on music content and user history. To the neural network may be trained to provide a regression build a user preference feature model, students often utilize model that can be used to predict piano performance Learning Skills Analysis (%) Learning Activity Analysis (%) 12 International Transactions on Electrical Energy Systems 10 20 30 40 50 Number of Students 10 20 30 40 50 Number of Students JAVA-PTMS IPINTSM JAVA-PTMS IPINTSM BPTM MPTM BPTM MPTM PTS PTS Figure 10: Student performance ratio (%). Figure 9: Learning behavior analysis ratio (%). 4.1.5. Teaching Evaluation Analysis (%). Traditional characteristics. Recommended music items are generated by methods of instruction often result in students’ lack of combining the regression model’s predictions of piano initiative when studying the piano. e students’ lack of performance attributes with a user’s stated preferences and interest in studying leads to fewer attempts to master the then using the recommendation algorithm module to build a piano and slows overall progress in their professional suc- list of music items in which the user would be interested. cesses. Students’ learning excitement may be considerably boosted by the incorporation and direction of multimedia technology and students’ cognitive abilities can be improved 4.1.4. Student Performance Analysis (%). Learning the piano while they are learning to play the piano. Figure 11 explores is not a one-time activity, and it often demands students to the dataset’s teaching evaluation analysis (%) [26]. have adequate knowledge and tracking complete recognition See equation (4) for output layer node outcome calcu- of the piano learners should need to have a specic skill play lated for teaching evaluation ratio, as well as student’s to achieve an overall performance level. e use of multi- performance. Teaching can be made easier with the help of media technology enables teachers to provide students with multimedia technology, which makes it possible to simplify a reasonably open educational environment and space where the teaching process. Some of the more abstract aspects of students will thoroughly understand piano knowledge. is piano playing and future technologies can be shown through positively impacts the composition of emotional resonance the visual display provided by multimedia, which helps to and energy-boosting exchange, which in turn creates con- prevent the issue of false cognition on the part of students as ditions for learners to enhance plenty of vitality for learning well as the preconceptions that can get in the way of im- and performance ability. Figure 10 elaborates on the student proving the eˆectiveness of piano learning. Table 1 shows the performance ratio (%) based on dataset [26]. results and discussion outcome. In Polyhymnia, a piano performance may be automated. An expressive performance may be guided by musical sig- nals, which can be interpreted in many diˆerent ways. 5. Discussion Because of this, we present adaptable parametric models that can be automatically interpreted. Expressive piano playing Using multimedia technology, this study explores the relies heavily on its polyphonic characteristics. Polyphony is concepts and characteristics of multimedia network teaching common in piano compositions. is article will discuss technology, outlining the components, classications, cur- them and refer to them as polyphonic expressiveness. rent state of development, and potential future applications. Performances with polyphonic expressiveness were found to Two features of the teaching content structure and content sound better than those without it, using a statistical model delivery applications illustrate the key technologies of polyphonic piano renditions. Based on the present state of employed in designing the network music education system. multimedia technology application in the design of inte- e many subsystems of the streaming teaching system are gration in piano instruction, this article focuses primarily on logically organized, and numerous sorts of technological the application structure and general design of multimedia challenges that must be overcome when using multimedia technologies in piano education. It concludes that multi- are examined. is paper proposes a system of networked media piano education is innovative and groundbreaking, multimedia instruction. To deliver educational services to and the article intends to establish the circumstances for its faculty, students system uses existing campus network in- smooth growth. frastructure software exclusively. is study’s integration of Learning Behavior Analysis (%) Student Performance Ratio (%) International Transactions on Electrical Energy Systems 13 References [1] L. Li, “Study on the innovation of piano teaching in normal colleges and universities,” Creative Education, vol. 09, no. 05, pp. 697–701, 2018. [2] L. X. Zhang, “e “four main factors theory” of piano teaching and its systematic thinking,” Advances in Social Science Ed- ucation and Humanities Research, vol. 322, pp. 359–362, 2019. [3] G. Comeau, Y. Lu, and M. Swirp, “On-site and distance piano teaching: an analysis of verbal and physical behaviours in a 50 teacher, student and parent,” Journal of Music, Technology and Education, vol. 12, no. 1, pp. 49–77, 2019. [4] Z. Ye Yang, “Modern piano teaching technologies: accessi- bility, eˆectiveness, the need for pedagogues,” Ilkogretim 10 20 30 40 50 Online, vol. 19, no. 3, pp. 1812–1819, 2020. Number of Students [5] J. Li, “Analysis of piano curriculum education and cultivation JAVA-PTMS IPINTSM of creative thinking ability,” Region - Educational Research and Reviews, vol. 2, no. 1, p. 6, 2020. BPTM MPTM [6] N. Lu and L. Dong, “e signicance and application of PTS Chinese piano works in piano teaching in colleges and uni- versities,” Frontiers in Educational Research, vol. 2, no. 4, Figure 11: Teaching evaluation analysis (%). [7] W. Qianfang, “Piano teaching in colleges and universities in Table 1: Results and discussion outcome. the environment of new media,” Higher Education and Oriental Studies, vol. 2, no. 3, 2022. Number of performance metrics Outcome (%) [8] Y. Wang, “Optimization of the music teaching management Learning skills ratio 97.6 system based on emotion recognition,” Computational In- Learning activity ratio 98.5 telligence and Neuroscience, vol. 2022, Article ID 4568041, 9 Learning behavior ratio 94.2 pages, 2022. Student performance ratio 93.8 [9] W. Ma, “Comprehensive evaluation system for realizing the Teaching evaluation ratio 90.3 ability of university piano teaching,” Mobile Information Systems, vol. 2022, Article ID 6895484, 8 pages, 2022. multimedia network technology and classroom instruction [10] D. Lu, “Inheritance and promotion of Chinese traditional may increase the teaching impact and provide a pleasant music culture in college piano education,” Heritage Science, teaching environment that encourages students to learn vol. 10, no. 1, pp. 75–10, 2022. independently and collaborate. ere are three levels in [11] Z. Chen, “Rethinking the university of the piano teaching which this article’s di culties may be found: instructors, model in the Internet era,” Frontiers in Art Research, vol. 4, students, and school administration. As a rst step, it is no. 3, 2022. [12] D. Wang, “Analysis of multimedia teaching path of popular recommended that educators thoroughly understand how music based on multiple intelligence teaching mode,” Ad- multimedia networks may be used to educate students and vances in Multimedia, vol. 2022, Article ID 7166569, 10 pages, assemble a team capable of overseeing the implementation of these networks in the classroom. e use of multimedia [13] C. Li, “A deep learning-based piano music notation recog- technology in piano instruction may help students overcome nition method,” Computational Intelligence and Neuroscience, limitations of time and distance, allowing them to get a vol. 2022, Article ID 2278683, 9 pages, 2022. deeper understanding of the art of piano playing and the [14] W. Gu, “Recognition algorithm of piano playing music in information associated with it. e experimental results intelligent background,” Mobile Information Systems, show that the proposed MPTM achieves the learning skills vol. 2022, Article ID 1245078, 11 pages, 2022. ratio of 97.6%, learning activity ratio of 98.5%, student [15] K. Lei, “e eˆectiveness of special apps for online piano performance ratio of 93.8%, teaching evaluation ratio of lessons,” Interactive Learning Environments, pp. 1–12, 2022. 90.3%, and learning behavior ratio of 94.2% when compared [16] X. Liu, “Research on piano performance optimization based on big data and bp neural network technology,” Computa- to other methods. tional Intelligence and Neuroscience, vol. 2022, Article ID 1268303, 10 pages, 2022. Data Availability [17] F. Wang, “e eˆect of multimedia teaching model of music course in colleges and universities based on classroom Audio e data that support the ndings of this study are available data mining technology,” Tobacco Regulatory Science, vol. 7, from the corresponding author upon reasonable request. no. 5, pp. 4520–4531, 2021. [18] Z. Nie, “Design and implementation of JAVA-based piano Conflicts of Interest teaching management system,” Educational Sciences: ’eory & Practice, vol. 18, no. 5, 2018. e authors declare no potential conƒicts of interest with [19] L. Changhan, A. Cleesuntorn, and S. Phongsatha, “A model of respect to the research, authorship, and/or publication of digital piano training system to improve the comprehensive this article. performance of pre-school education major sudents: a case Teaching Evaluation Analaysis (%) 14 International Transactions on Electrical Energy Systems study at a public university in Hunan, China,” AU-GSB e-Journal, vol. 13, no. 2, pp. 49–56, 2020. [20] L. Zhu, T. Phongsatha, and A. Intravisit, “A blended piano teaching model for non-piano music major students in Hunan city University,” AU-GSB e-Journal, vol. 13, no. 2, pp. 38–48, [21] A. Yonathan, “Organic concept in rolf-dieter arens’s piano teaching strategy,” Malaysian Journal of Music, vol. 11, no. 1, pp. 1–13, 2022. [22] C. Shuo and C. Xiao, “*e construction of internet+ piano intelligent network teaching system model,” Journal of In- telligent & Fuzzy Systems, vol. 37, no. 5, pp. 5819–5827, 2019. [23] L. Unlu, ¨ “A qualitative study of the perspectives of music students on distance piano education,” Education Quarterly Reviews, vol. 5, no. 1, 2022. [24] J. Fu, “Analysis on the piano teaching mode of flipped class in higher education,” Frontiers in Educational Research, vol. 3, no. 14, 2020. [25] https://www.kaggle.com/datasets/davidbroberts/piano-triads- wavset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Transactions on Electrical Energy Systems Hindawi Publishing Corporation

Training Strategy of Music Expression in Piano Teaching and Performance by Intelligent Multimedia Technology

Loading next page...
 
/lp/hindawi-publishing-corporation/training-strategy-of-music-expression-in-piano-teaching-and-54gSmKpC8h
Publisher
Hindawi Publishing Corporation
Copyright
Copyright © 2022 YunDan Zheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
eISSN
2050-7038
DOI
10.1155/2022/7266492
Publisher site
See Article on Publisher Site

Abstract

Hindawi International Transactions on Electrical Energy Systems Volume 2022, Article ID 7266492, 14 pages https://doi.org/10.1155/2022/7266492 Research Article Training Strategy of Music Expression in Piano Teaching and Performance by Intelligent Multimedia Technology YunDan Zheng , Tian Tian, and Ai Zhang Academy of Arts, Chongqing College of Humanities, Science & Technology, Hechuan, Chongqing 401524, China Correspondence should be addressed to YunDan Zheng; noreen_bradford@stu.centralaz.edu Received 17 June 2022; Revised 12 July 2022; Accepted 18 July 2022; Published 28 August 2022 Academic Editor: Raghavan Dhanasekaran Copyright © 2022 YunDan Zheng et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. st Teaching using a multimedia technology in the 21 century affords the possibility of developing novel instructional strategies and paves the way for the all-around extension of musical educational functions. *e importance of multimedia teaching technology in piano instruction has started to emerge in our country and society due to the ongoing development of this kind of technology in music educational institutions here. *e conventional method of teaching piano has several drawbacks that may be mitigated by using one of the several alternative methods of instruction for the instrument, especially in light of the ongoing advancements in science and technology. A pianist’s methods of expression are the tools they use to convey their thoughts and emotions about a piece of music to the audience. Teachers may demonstrate their musical skills to students and they must immediately focus on a musical expression which is vital for performers. In this paper, the Multimedia-based Piano Teaching Model (MPTM) has been proposed to improve the piano teaching quality. Traditional piano instruction is improved and developed using multimedia technology in this article. *e Internet education model is used for teacher assessment, and the systematic way representing piano teaching combines different music educational materials. It begins building a sufficiently broad music network infrastructure resource sharing framework and benefits society’s amateur music literacy. *e use of machine learning in students’ concrete piano instruction has the potential to thoroughly promote contemporary piano instruction and enhance the overall quality of in- struction. To begin, an explanation of the intelligent piano’s features and capabilities is provided. *e neural network is used to suggest a technique for detecting a piano note on a set. *e network can assess the input piano music signal’s time frequency by translating the original time-domain waveform into the time-varying frequency distribution. Intelligent piano instruction analysis can effectively achieve the overall optimization of piano performance. *e test results show that MPTM has a significant role in boosting the desire to learn to play the instrument. *e experimental results show that the proposed MPTM achieves a learning skills ratio of 97.6%, a learning activity ratio of 98.5%, a student performance ratio of 93.8%, a teaching evaluation ratio of 90.3%, and a learning behavior ratio of 94.2% when compared to other methods. pianos may be used to educate online students on how to 1. Overview of Multimedia-Based Piano play the piano, gradually allowing them to realize its in- Teaching Model (MPTM) telligence [4]. As a result of inefficient and chaotic management *e demand for piano instruction is increasing, and the practices, the growth of piano teaching activities is severely number of piano instructors is increasing [1]. People’s eyes hampered by the conventional manual techniques used in are being opened to a new generation of multimedia piano traditional piano education management [5]. Lessons in the instruction resources like technology and the Internet, traditional “teacher with piano content” method fall short of which continue to advance [2]. Multimedia piano instruc- student expectations. Developing piano instruction will be tion is widely disseminated through the Internet in several more effective if multimedia technology is used [6]. Learners methods. *is rich and diversified teaching method is be- are provided less time to play the piano and cannot obtain coming more popular [3]. Internet-connected intelligent 2 International Transactions on Electrical Energy Systems (ii) Piano instructors and students worked together to sufficient practice, making it difficult for students to apply the information they have gained from their professors to develop an assessment index for the neural net- works used in this model. actual piano playing [7]. Teachers’ energy and time are limited and because they cannot identify the challenges that (iii) *e NN’s training has been completed and the every student faces, it is difficult for a teacher to offer a lesson piano teaching procedure has been verified using that is individualized for each student’s level of proficiency piano performance data. [8]. In addition, because students in big classes have varying *e overall organization is as follows: Section 1 discusses levels of competence, learning process of pupils is made the introduction of piano teaching, Section 2 deliberates the more difficult by the teaching environment [9]. related works, Section 3 explores the proposed MPTM with *e use of multimedia technology in piano instruction at machine learning techniques, Section 4 explores the results universities and colleges represents a significant revolution in and discussion, and Section 5 demonstrates the conclusion music teaching and educational method in colleges and of the paper. universities, indicating that music education has entered a new age [10]. *e use of multimedia technology in college and university piano instruction helps change abstraction into 2. Related Works concreteness, which is important for boosting students’ ca- pacity to enjoy music [11]. *e use of multimedia technology JAVA-based Piano Teaching Management System (JAVA- in piano instruction accomplishes the union of technology and PTMS) for analysis of the status quo and problems of piano science which can realize all-dimensional high-efficiency ed- teaching informatization was described by Nie [18]. By ucation [12]. It helps increase the piano’s attractiveness to analyzing the current reasonably mature technological pupils which is important for increasing students’ interest [13]. framework and programming language, this study chose to Machine learning is used to analyze multimedia piano implement a B/S system architecture and an SSH framework instruction performance information which offers decision for the major body of the system. JAVA was used to build assistance for teaching managers and is most important for and construct the system based on the structural design idea. improving multimedia piano teacher performance [14]. In *ere has been a successful implementation of the finished the functional design of the piano teaching operating system, piano instruction administration system. As a result, the neural network (NN) music visualizations are a crucial school’s limited piano teaching resources cannot fulfil the contribution [15]. Students can watch their piano perfor- growing demand from students with such inadequate piano mances, allowing them to fully comprehend the information foundations, distinct understandings, and preferences for in the song they play. Machine learning techniques and NN the piano. *e findings of the experiments reveal that a more representation learning are widely utilized in the age of stable system has a quicker reaction time. information processing jobs [16]. For advising the student in Digital Piano Training System based on Technological playing practice, a music assessment system based on the Pedagogical Content Knowledge (TPACK) for analyzing NN model is determined [17]. preschool students’ piano performance was discussed by Students’ capacity to enjoy music may be improved via Changhan et al. [19]. 30 of the university’s preschool multimedia technologies in college and university piano students were randomly chosen and offered a one-month instruction. MPTM emphasizes practical and deliberate trial of the Digital Piano Training System (DPTS) tech- difficulties that may not occur in smaller classes. A range of nology at a public institution in Northeast China, which supervised, unsupervised, and semisupervised machine provides digital piano lessons and has 360 students en- learning algorithms have attracted much interest in this rolled. *erefore, it was established and validated that the domain. *is study proposes a specific machine learning DPTS has been the most effective piano teaching instru- approach for evaluating the possible association of piano ment for preschool pupils. As a consequence, institutions instruction. *is study examined two major components of should consider using DPTS as one of their piano teaching piano music learning. Machine learning methods may im- methods. prove piano music courses for various learning styles and Blended Piano Teaching Model (BPTM) for students at audiences. *e automatic creation of lesson plans that may the University of Hunan City who are not majoring in instruct music fans to play their favourite instruments Filipino music should take this course deliberated by Zhu provides access to distinct learning styles, diverse musical [20]. Regarding sight-reading, scales and arpeggios, etudes backgrounds, and talents. Machine learning is used in music and piano pieces, the experimental group utilizing the automated recording technology to determine the imple- BPTM model outperformed the control group statistically. It mentation principle and legislation of a piano automatic was inferred and proven that the BPTM was an effective recording system. Music, rhythm, and instruction may all teaching instrument in piano instruction for nonpiano benefit from the integration of piano music technology, majors. which is the focus of this course. Piano Teaching Strategy (PTS) for teaching organic concepts was expressed by Yonathan [21]. In-depth inter- views with students and participant observation were the 1.1. !e Main Contribution of the Study main modalities of data gathering. Setting objectives, (i) *is study presents an evaluation technique based modelling, listening, visualization, breakdown of the musical on the NN model for students’ playing practice. structure, and subdivision assistance are among Arens’ International Transactions on Electrical Energy Systems 3 teaching tactics. Arens teaches that procedures and creative performance, complicated networks and multimedia tech- interpretation are the same rather than taught separately nology have been studied extensively. *is article aims to which is a huge difference. identify, examine, investigate, and assess current piano *e Internet with Piano Intelligent Network Teaching training techniques to maintain only those that comply with System Model (IPINTSM) for using Internet technology was contemporary theories of learning, educational standards, explored by Shuo [22]. *e acoustic and multinote models of and the distinctive qualities of the piano discipline. Before the HMM with many tones were created with the aid of the introducing various network training instances for piano Internet. An IPINTSM is created after determining the instructors to use and investigate, the pros and disadvan- match between the testing audio and the multinote models. tages are considered. Switching to a more student-centred *e most accurate multinote identification rate was found teaching style will improve the initial single-class teaching when the multistate recognition system included seven method. *e innovative teaching approach allows students states. When the IPINTSM multinote recognition state is 7, to learn the piano at their speed via multimedia and network the IPINTSM has generated fresh inspiration that is more technology. In this study, the course material is digitized and accurate. represented using machine learning algorithms, and the Students’ perspectives on distance piano education were systems that allow access and availability of the produced qualitatively analyzed by Unlu¨ [23]. *e research explores content are maintained using the NN and machine learning how university students who get piano instruction through technologies with or without Internet assistance. When distance education are influenced favorably or adversely. these two technologies are used with piano instruction, Qualitative research was conducted utilizing the phenom- higher music education on machine learning may be more enological design technique to achieve the results. *e effective. study’s findings indicated that students did not find piano Offline instruction is face-to-face piano teaching in a lessons through distance learning advantageous in general smart classroom shown in Figure 1. Online teaching com- and that students’ overall performance suffered as a plements offline teaching using microlecture videos, distant consequence. education, and smart partner training technologies. Eye- Microlecture Flipped Classroom Piano Teaching Model strain is likely to develop when there is a significant contrast (MFCPTM) for analysis of piano teaching mood was ini- in brightness between the musical notation and the sur- tialized by Fu [24]. *is essay delves deeply into the theory of roundings, such as when the instrumental music is darker the microlecture flipped classroom and explores the benefits and the environments are light (similar to using a smart- of incorporating it into the practice of piano instruction. *e phone in the dark). A piano light such as the LED piano light excellent method of MFCPTM to optimize piano teaching in is required to correctly illuminate the sheet music and the the microclass flipped classroom can benefit colleges and piano keys. Light Emitted Diode (LED) indications in the institutions and provide a useful reference for strengthening piano game mode motivate kids to practice. Students may the piano teaching in the micro-class flipped classroom in study music in a pleasant and joyful setting, increasing their colleges and universities. sensitivity to music and enthusiasm for learning and making Linna et al. [25] expressed the Wireless Network in Piano piano instruction dull. With the introduction of piano Music Teaching based on artificial intelligence. Artificial teaching apps, the intelligent piano may be linked to the app intelligence (AI) advancement offers a new path for the old software through smart devices to enable human-piano educational approach. In the opinion of piano majors, a interaction. *e Garage Band application program may smart piano benefits novices and those with limited piano create music without requiring professional Musical In- skills. On the other hand, high-level students will not benefit strument Digital Interface (MIDI) equipment, giving music from smart pianos. Teachers should use various techniques enthusiasts more options. Synthesizers, samplers, and to meet students’ needs at various skill levels in the class- computers all can interact with one another via the use of room. Finally, based on the existing state of the smart piano, MIDI signals. MIDI is a method for connecting devices that countermeasures and proposals for the future development generate and control music. *e MIDI controller or key- of the smart piano in piano music instruction are proposed. board can also imitate a violin, flute, bagpipe, or any other Based on the analysis of existing methods, piano instrument for which samples can be found. Familiarity with teaching needs to be improved more effectively. *e pro- the controls will have a pleasant manner of playing them into posed method, MPTM, utilizes the neural network to an- the music. Built-in systems of intelligent pianos and network alyze the student’s performance and predicts the teaching piano classrooms provide recorded or live video instruction quality to deliver the desired outcome. courses. Teachers and students may communicate through network voice and video. Teachers and students interact via example, dialogue, and visuals. 3. Multimedia-Based Piano Teaching Smart pianos give scores to students based on their pitch, Model (MPTM) rhythm, and strength. Each student understands their learning circumstances and inadequacies. Student assess- With the advent of multimedia network teaching as a ment is three-staged. First, first-time intelligent piano users contemporary teaching mode, new educational techniques must examine their piano-learning level, interest, and future may be developed and educational functions can be ex- aspirations. Students without a piano basis should start with panded in all directions. With piano education and 4 International Transactions on Electrical Energy Systems Piano performance and teaching evaluation Music appreciation Online and offline teaching Distance Offline teaching Online teaching of intelligent piano learning Solfeggio Intelligent Music reading Musical Micro lesson sparring preview teaching composition Figure 1: Hybrid teaching of o“ine and online piano teaching model. the basics, while those with a foundation may pick the test’s E(y) ly, di culty level. Second, the repertoire’s song, rhythm, and power are rated. ird, students’ results determine a learning E(y) σ, k, wheny ≥ θ, (2a) plan for their present level. Smart piano assessment helps instructors train students according to their potential. El- E(y) σy, wheny − θ < y < θ, ementary learners benet from one-to-many group edu- cation, whereas high-level learners prefer one-on-one. In E(y)−σ, k, whenyy ≤ θ, each step of student learning, sensible assessments of their (2b) E(y) σ, wheny ≥ θ, learning circumstances may drive learning passion via group competition. e smart assessment module may convey E(y) ϕ, when y ≥ θ, (2c) kids’ music and class successes to parents through the network so they can monitor their students’ development. Students may also self-evaluate and evaluate based on in- E(y) α + . (2d) structor feedback. Teachers may master students’ learning 1 + exp(−dy) and make modications and innovations based on their As discussed in equations (2a), (2b), (2c), and (2d), suggestions. transfer functions have been deliberated. Equation (2a) Simulating real neurons is a complex task that takes denotes the linear function, (2b) shows the nonlinear slope more than mere easiness; it demands an understanding of function, (2c) shows the step function, and (2d) represents the fundamental properties of biological neurons. As with the t-type function, where σ is a transfer function variable, real neurons, the “connection strength” of an articial θ is an angle, a is a transfer function variable, ϕ is a step neuron may be determined by the weighted total of the input function coe cient, and l is a slope function variable. A signals from other neural connections, each of which rep- network’s eˆectiveness might be severely aˆected by resents a possible output, and this sum denes the neuron’s transfer functions that exponentially increase network active state. e Z ,Z , ... ,Z coupling weighting matrices 1 2 m input; hence, the selection of transfer functions for various correlate to the y ,y , ... ,y feature vectors, which com- 1 2 m application areas of the NN model is crucial. A lot of people prise the M inputs. Neurons are depicted by input and are using the new feature. In general, the hidden layer connection vectors Z and Y, respectively, which show the employs a t-shaped function, and the output layer utilizes a cumulative inƒuence of the input signal on the neuron’s linear one. output. Let us assume that the neural network has m nodes in the input layer, that the hidden layer has p nodes, that the output snet y z . (1) j j layer has m nodes, and that there is a weighted correlation j1 between the weights of U and Z between the input and jl jl As found in equation (1) neuron input signal has been hidden layers. Its transfer functions are E (y) and E (y), 1 2 and the output of the node that is on the buried layer is described. After receiving input from the network neurons represented in E (y). should provide the desired results. Whenever the cumulative eˆect of its input signals hits this threshold, each neuron is in an excitation state. In this suppression state, the neuron does     W  E  U y . (3) l 1 jl j not respond to the input signal. E(y): out Enet represents j0 the transfer function for articial neurons, and R the output As shown in equation (3) hidden layer node has been of neurons. Linear, nonlinear ramp, step, and t-type transfer demonstrated. e output layer node’s outcome is repre- functions are all examples of typical governing equations, as sented by the following expression: shown in the following equations: International Transactions on Electrical Energy Systems 5 m Furthermore, we found that CiP may help the instructor ⎛ ⎝ ⎞ ⎠ x � E 􏽘 Z W . execute even an unprepared piece to their delight. As a (4) j 2 jl l j�0 result, they are free to focus their full attention on musically expressing themselves. *is output information is being In equation (4), output layer node outcome is calculated for recorded by the piano that is attached. Before beginning to teaching evaluation ratio, as well as student performance. *e play it is important to input into the computer the sequence rhythm quality is extracted from the music score bar by bar, of pitches that will be used in the composition that will be using each musical section. Each performance segment may be played. *e MIDI note numbers are what are used to specify precisely pinpointed using the score split into music subsec- the pitches. During the performance, the computer will tions. *e current bar’s rhythm and beat may be determined replace the note numbers that were fed into it with the note based on the performance time’s placement. It is the rela- numbers that are being played. tionship between the length of a given note, also known as the At last, the computer will output the note numbers that time point, and the duration of the pronouncing of each note; have been replaced and then feed those numbers into the as shown in the picture, its scoring with four quarters sounds as tone generator. In light of this, the CiP can always output one measure. By isolating the tempo from the current bar, sound at the correct pitch, even if the performer touches the rhythm will be able to evaluate the player’s capacity to keep up wrong keys. At this time, CiP is capable of handling mo- with a moderate beat. Note the times at which the player nophony. CiP will not react to any key-down events within depresses and releases each of the four notations while the first fifty milliseconds after a previous key-down event. maintaining alignment with the first note played at the be- *is is done to account for accidental touches. ginning of each bar. Following notes are dependent on how On the other hand, note-on velocity (when the key is long the preceding note has been spoken, according to the pressed down), note-off velocity (when the key is pressed rhythm’s qualities. As previously stated, subsequent notes up), and pedal messages are generated while the player would have mistakes if the prior note was improperly handled. performs. *e different parts of the instructor’s musical Various weights must be given to different notes to assess the knowledge are represented in the same way the instructor level of rhythm mastery. To determine how well the rhythm of conveys them. *is is the first system developed to learn and this portion of music is understood, divide the standard value predict polyphonic expression in piano music with various by the pronunciation point for each note and multiply the performing styles. Polyhymnia can generate expressive result by the weight of that note. polyphonic piano performances using music scores, so that 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 it may be utilized as a computer-based tool for expressive 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 E(y) � 􏽘 􏼐T − A 􏼑 + 􏽘 P 􏼐F − C 􏼑 P . (5) 􏼌 j j 􏼌 j􏼌 j j 􏼌 j performance. Polyhymnia can completely computerize an expressive performance on the piano. *e fundamental As obtained in equation (5), rhythm music has been structure of an expressive performance may be deduced determined. *is measure’s note sequence number is j. *e from musical symbols, and these symbols themselves can be number of notes in this measure time the player pushes interpreted in several different ways. Polyhymnia is an releases a key t and standard release time is all included in automated piano performance system that can learn and this measure’s notes list; C is the usual release time and P is anticipate polyphonic expressiveness and interpret musical the amount of weight that corresponds to the different notes. symbols mechanically. According to the results of experi- For each measure, the note volume is quantified and ments involving produced performances, the system pro- recorded. In terms of rhythm, the first note of each bar is duced performances with polyphonic expressiveness and critical. *e beat characteristic of this bar may be extracted dynamic sound, and human listeners could discern between using the same procedure and various weights for different the various performance styles. Machine-rendered piano notes as shown by the following: performance might benefit by modelling the hierarchical 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 E(y) � 􏽘 􏼐B − A 􏼑 P . (6) structures of a specific composition. Interactive performance 􏼌 j j 􏼌 j may be achieved by extending Polyhymnia model param- Beat feature function is given in equation (6). *ese eters via an interface. *e use of adaptable parametric variables are note count j, note volume (B), bar length (A), models for their automated interpretation is advocated. weighted value (P), and chord volume (A) with j being the Since piano music is often polyphonic, emotive piano number of notes in each bar. performances must include polyphonic characteristics. *is *e structure of CiP is shown in Figure 2. *e “Coloring- study refers to musical expression with such characteristics in Piano” (CiP) is a novel musical instrument in which as polyphonic expression. *e suggested statistical model- traditional pianos do not require their players to reproduce ling of polyphony piano renditions demonstrated that the melody line meticulously. Using CiP enables them to performance produced with polyphonic expression looked focus directly on the musical expression crucial for the better than presentations without it. *is has been accom- performers, allowing the instructor to demonstrate their plished by demonstrating that polyphonic expression can be musical understanding to the student. *ere are a series of modelled statistically. *e results of experiments conducted trials to compare the performance of Coloring-in Piano with on performances created by Polyhymnia using a variety of that of a regular piano. It is clear from these findings that a compositions suggest that they had polyphonic expres- pianist using CiP can play just as well as one using a tra- siveness and sounded expressive. *e majority of these ditional piano. systems focus on discussing different interpretations of 6 International Transactions on Electrical Energy Systems Inputs by Output by Polyhymnia Student System Melody Piano MIDI Expression Score Estimator Harmonic Basic Deviation Expressive Expression Tempo Generator Performance Estimator MP3 Musical Scaling Symbol MP3 Ratio Interpretor Figure 2: Tune generating. monophonic tunes. Still, polyphonic interpretations have speed at which a note-on message descends a key is almost received less attention owing to the computational com- identical to the note’s volume. plexity and enormous quantity of required data. *e piano’s control flow diagram shows a subtemplate for *ere is little discussion over the automated interpre- control in Figure 3. *ere are three distinct sorts of piano tation of musical symbols based on extremely basic prin- playing: the piano itself, the piano’s white key, and the piano’s ciples, since they enter a MIDI-like score. A music expressive black key. *e piano type is responsible for managing all performance may be provided by certain commercial no- aspects of piano performance, including the instrument’s tation software. It is not known how they likely come up with startup, playing, and sound. *e piano white key and the musical expressions that they use basic criteria to under- piano black key may be used as a single white or black piano stand musical signals. Polyhymnia users must enter a piano key, respectively. *e piano-playing module, the pianist audio score in MusicXML format to get an expression piano processing module, and the piano screen and controller make performance. MusicXML, unlike MIDI, can digitally encode up the piano teaching system. Figure 3 depicts how the almost all musical symbols. A mathematical model is used to system’s piano display and control module completes its tasks. analyze encoded musical signals and create a variety of *e process begins with the activation of the virtual piano and possible interpretations for each symbol. Conditional then moves on to the initialization of the piano’s variables, the Random Fields (CRFs) are used to teach and produce showcase of the piano interaction, and finally the tracking of polyphonic expressiveness for polyphonic piano renditions. the piano audio input operations. *e piano interface will be Scaling ratios influence the expressiveness of the resulting redisplayed once the operation is processed and detected music. In addition to MIDI and MP3 files, the system enables while using the piano interface. expressive performances. Following the activation and initialization of the ana- To determine gap value using the performance data in logue piano, the piano disk interface will be shown and Musical Instrument Digital Interface (MIDI) format, the playing will be observed. After the detection of playing interonset interval (IOI) may be calculated as follows: actions variety of playing events will be handled to bring IoI � S − S . (7) about an update to the piano’s user interface that will show j non(j+1) non(j) the final effects. *e piano audio processor module is re- As described in equation (7), IOI has been deliberated, sponsible for simulating the sound of a piano and improving where IoI refers to the j-th information output indicator, j such a sound. Applying certain treatments to the sound will S refers to the emitted time of the j-th note-on message non(j) become richer and more pleasant, significantly increasing S , and S refers to the emitted time of the non(j) non(j+1) the auditory experience that the instructor and the students (j + 1)-th note-on message non(j + 1). *e value of the gap have. *e addition of a module that generates a soft sound may be found by doing the following: source will contribute to the production of a better voice effect. gap � S − S . (8) j non(j+1) noff (j) An observation of understanding has been made and is As found in equation (8) gap value has been identified, particularly true when compared to an investigator to aid where S is the note-off message’s emission time and comprehension. A central location provides a great position; noff (j) S is the (j + 1) note-on message’s emission time a disadvantageous location offers an important comparison non(j+1) having gap � S − S . As a result, if the j-th note point. After learning about scribe searches learner adopts a j non(j+1) noff (j) has a positive gap performer has shortened it. In addition, new point of view on the world and records that perspective the MIDI note-on message’s velocity data are retrieved. *e in the dataset sin(c) � n /n and sin(β) � n /n . 1 j 2 j International Transactions on Electrical Energy Systems 7 Start the virtual piano Initialize the piano parameters Listening Piano zoom, Display piano Piano zoom, pan translation interface control interface operation Yes operation processing No Figure 3: Piano control ƒow chart for controlling subtemplates. Remove g from equation (9) to get the most basic As deliberated in equation (11) key function has been knowledge of the item in the central library. demonstrated. When deciding how to implement remote music education, the goal is to teach participants in an n n 1 2 g   sin(c) +  sin(c) . j organized and simplied manner to integrate knowledgeable (9) n n j j j1 j1 predened attitudes and morality adapted for specic thoughts and actions. is goal n V + n V /nv is achieved 1 1 2 2 As demonstrated in equation (9) central library has been by machine learning teaching techniques. described. A survey questionnaire, developed, integrated, and scored after the discovery of the teaching materials, will n V + n V 1 1 2 2 Learning Behavior   n V , j j investigate the impact of distant location music piano ed- n V j j (12) ucation on machine learning and networked instructional methods on the unique educational capabilities of learners. n V + n V . 1 1 2 2 Objectivity, e ciency, and quantiable n /V analysis are all j j advantages of this approach. It is a common way to gauge a As initialized in equation (12), learning behavior has person’s capacity for self-directed learning, as an alternative been calculated. is belief is shared by educators who feel to specic learning activities, such as that learning  n V is more than simply a means to an j j end. Learning activity   n V sin(c). (10) j j j1 n V + n V 1 1 2 2 Learning skills Rate n V + . (13) j j As obtained in equation (10) learning activity has been n V j j j discussed. Piano music learning sin(θ) is a way of teaching n(c − σ) which includes an educational system to recognize As expressed in equation (13) learning skills rate has the unjustied progression of sin c to σ such a new economy been formulated. Learning skills |N(m)| such as notoriety, that has nally lost its key function in continuous learning as recognition, the capacity to grasp, and encouragement may proven by be used by students to increase their skill set. sin(θ)  sin(c − σ) |N(m)|   observing|n(N(m))| + n V . (14) j j j j sin c sin σ + tan c tan σ (11) As shown in equation (14), to achieve the primary goal of n V n V n V + n V 1 1 2 2 1 1 2 2 piano music education based on machine learning and 5G +  . n V n V nv network expansion, a socialist and optimization strategy j j j j 8 International Transactions on Electrical Energy Systems communications technology for data processing and the must be underscored to break free of organizational limi- tations and identify an effective structure for the delivery of transmission of information. With machine learning, a method in which the processing unit is supplied with the the indispensable university degree. See Figure 4 for multimedia intelligent piano teaching network edges, the efficiency of the computer processing system. Here, we provide a system for intelligently teaching may be improved. *e information must first be sorted and piano using the multimedia with teaching system, which evaluated to determine how the data are distributed. Mul- includes a system for teachers, one for students, and one for timedia-assisted piano teaching and classroom management back-end administration (Figure 4). To assist students in can be improved by using data characteristics, rules, and improving their musical knowledge and their ability to play connection structure at the edges. According to the infor- mation provided by the evaluation feedback, it is necessary chords, piano teachers use clever methods of instruction. Instructors must devise instructional strategies that use to find timely solutions to the various issues that have arisen in the teaching, further optimize the different aspects of students’ strong sense of wonder, curiosity, and inventive- ness to keep their students engaged and motivated to learn. multimedia-assisted piano teaching and give full play to the obvious benefits of multi-coal-assisted piano teaching, and Teachers may play an imported animation of piano lessons on the smart piano classroom’s screen using the system’s establish the underpinnings for increasing the quality of teaching feature as a second option. As a result, students can piano instruction. Information literacy for educators and the make notations on a smart music score shown through promotion of educators’ personal growth and development multimedia by their instructor. *e instructor shows how to by teaching the piano with the assistance of multimedia play a chord and teaches the proper technique. A standard combine the benefits of both conventional teaching and performance video is also entered into the system so stu- multimedia teaching and play a significant role in enhancing the quality of instruction. However, at the same time, more dents can view it repeatedly after class. In addition, teachers must show and explain the fingering animation video and stringent standards should be put up about the qualifications and skills of instructors participating in multimedia piano the associated knowledge points. *e instructor checks whether each child’s hand form and chord are correct instruction. With the assistance of edge computing priority to offer educators helpful information throughout evaluat- throughout the playing process and encourages fingering. Sensible piano instruction may improve students’ passion ing students, this may be done via the evaluation system’s for piano practice and their capacity to study independently. orientation, diagnosis, and quick feedback. Analyzing as- Using their accounts in the intelligent piano application, sessment data, it is possible to identify instructors’ obstacles students may log in and practice at their own pace under the and inadequacies in multimedia piano education. To assist guidance of their parents in the intelligent piano class. teachers in enhancing their knowledge of multimedia in- By employing data acquired via research, an evaluation formation, emerging technology, and capacity for classroom integration to foster both the self-improvement and im- aims to enhance scientific methods and researcher decision- making. Edge-commuting can enhance this assessment. proved performance of piano players, Figure 5 displays the assessment index for the multimedia-assisted piano in- Data analysis will be more efficient as a result of the edge- enabled approach. *e assessment information facts do not struction approach. come into action by themselves. Proper processing of as- *e use of multimedia education as an additional tool for sessment data and feedback is required. *e feedback as- piano instruction has revolutionized and reshaped the way sessment information plays a vital part in making the students learn the fundamentals of piano instruction. It may evaluation process successful and feasible. *e development help students learn the fundamentals of piano theory and of multimedia-assisted piano education is a complicated and play more quickly and efficiently. An autonomous piano organized undertaking strongly tied to the relevant theo- performance system should handle various unidentified retical research, technical advancement, and teaching piano compositions. *ere was evidence of polyphonic expressiveness and expressive sound in the performances practice. Many factors influence its usefulness as a teaching tool, including the quantity and quality of information lit- made by Polyhymnia using diverse compositions. A piano piece may be played in a variety of different ways. It is eracy teachers and students, as well as access to teaching support platforms. *e MPTM team is now in the process of possible to create a wide range of models by practicing determining the best ways to teach students. various performance styles. Polyhymnia’s experimental When designing multimedia-assisted piano instruction, findings on various performances show that each trained employing massive machine learning technologies to study model reflects the performance style in the training set. the factors that influence the effectiveness is a good place to Taking piano solo lessons may be arranged to be one-on-one start. *e acquisition of useful knowledge is of the utmost or in a group setting to integrate smart piano technology with more conventional piano instruction methods. As an importance to enhance the standard of multimedia in- struction, with responses to questions concerning the experiment, a teacher and an intelligent piano may work together to collect data for six weeks to evaluate how well the evaluation, analysis, and prompt alteration of instructional methods. In today’s information society, the swiftness with class is progressing and the overall quality of the material taught. Lessons on the intelligent piano will be taught in which feedback can be provided and the efficacy with which information can be transmitted both speed up the process of groups during the first and second weeks, lessons on the social evolution. During the assessment process, it is im- intelligent piano will be taught to students individually portant to factor in the potential benefits of adopting data during the third and fourth weeks, and lessons on the International Transactions on Electrical Energy Systems 9 Teacher account number Attend class and lesson preparation Intelligent piano Teacher Back-stage teaching system Module appreciation Student account and class organization number Effect of piano practice Class account number Students Basic teaching Composition and solfeggio Playing the piano and understanding music Figure 4: Piano display control subtemplate. Evaluation Evaluation IndicatorData Indicator Core Processing Data Processing Piano Pre-image Result Check Result Teaching Library Library Library Piano Storage Business Metabase Teaching Upload Synchronization Basic Check Rule Multimedia Result Feedback Library Service Library Management Multimedia Statistical Business Piano Teaching Indicator Information Basic Library Base Information Identity Check Verification Historical Information Access Service Area Figure 5: MPTM-based teaching evaluation. 10 International Transactions on Electrical Energy Systems traditional piano will be taught to students individually chord, whether it is sharp or flat, whether it is major or during the fifth and sixth weeks. *e foundation is the minor, and its location on the keyboard may be found in the Chord column of the table. *e additional column labelled regular stepwise and complementing instruction throughout the teaching process. *e benefits of this new unified “Note” contains the individual notes that make up the triad, teaching approach will be reflected in the final evaluation of arranged in increasing order (https://www.kaggle.com/ students’ degrees of completion, which will be determined datasets/davidbroberts/piano-triads-wavset). by combining the conclusions formed from the feedback *e number of students is on the x-axis and y-axis in- from students weekly. Figure 6 depicts the intelligent piano cludes learning activity, learning behavior, learning skills, lesson encounter process in which the students participate student performance, and teaching evaluation analysis based during the specific trial. on comparative analysis of JAVA-PTMS [18], BPTM [20], *e performances of various piano compositions with PTS [21], and IPINTSM [22] with our proposed method MPTM. different trained models showed polyphonic expressiveness and sounded expressive, according to experimental data. It was shown that human listeners could tell the difference 4.1.1. Learning Skills Analysis (%). Students who get their between the models trained in different performance styles piano instruction through multimedia have more oppor- and the models taught in the same style. tunities to practice independently while following the teacher’s explanations, enhancing their learning efficiency. 4. Numerical Analysis Learning to play the piano aids in the development of previously undeveloped neural connections between the Analysis of the real dataset demonstrates that the suggested hands and the brain. Playing this instrument requires strong, approach is feasible and effective. According to our findings, flexible hands that can perform of their own will. Studying to the potential usefulness of multimedia-aided piano in- play the first scale or chord on the piano is a crucial part of struction assessment data is by employing the degree of learning music theory. With one-on-one teaching, a teacher impact as the measure of association rules. According to the has the luxury of extra time to construct a lesson plan findings, it is out of the question to utilize the degree of specific to each student’s needs and abilities at the piano. impact as an association rule metric to uncover the potential Teachers may adjust their lesson plans depending on the use of multimedia piano instruction assessment data. Mu- progress and results of their students in real time. Figure 7 sicians now have a computer-based tool for crafting ex- shows the learning skills ratio (%) based on the dataset. pressive, polyphonic piano performances to Polyhymnia, As expressed in equation (13) learning skills rate has our automated piano performance system. Piano music may been formulated. *e advancement of the educators’ music be created using digital symbol translations and simulation playing skills will, in turn, enhance their capacity to rec- approaches of intellectual framework that account for ognize elegance, leading to a virtuous cycle that will en- polyphonic qualities. According to experiments, perfor- courage the students’ piano learning. *e improved mances of different piano compositions with various performance of the educators’ ability to enjoy beauty can training images showed polyphonic expressiveness and drive the learners to build their playing skills. sounded expressive. To make things more interesting, the models were trained in different performance styles and 4.1.2. Learning Activity Ratio (%). Individual instruction is were well distinguished by trained listeners. widespread in the activities of college piano teaching. Most individual teachings need the students and the instructors to 4.1. Dataset 1 Description. When developing a Digital Signal finish the instruction in a generally closed-off setting. Re- Processing (DSP) project for a Virtual Studio Technology nouncing the space limitations of the traditional teaching (VST) plug-in, we realized the advantages of digital tech- model teaching activities and multimedia technology can nology in music teaching. *e plan was to employ AI/ML to help students understand the piano theory and practice ability. *is is helpful for students to practice pertinence develop a product. 432 Wav files of piano triads over six octaves are included in this collection. A wave file represents during the practice. All-dimensional audio-video impact may be used to increase students’ emotional and knowledge the 12 major, minor, and diminished triad chords in its root and first inversion. 32 bits, 44 kHz mono, about 520 K size, experiences in the music classroom. Teaching in a more and 3-second duration are the sample formats. Around interactive approach like this necessitates that instructors 200 MB is the overall size of the wave files. Trios.csv is the begin with the facts and look forward to exploring new ideas. CSV file containing the chords’ names, octaves, and in- *e use of multimedia technology does not make it simpler versions. *e notes that make up each chord are also in- for students to locate a wide variety of high-quality resources cluded. Two underscore characters separate the chord names and educational materials about the piano enhance the into four sections. Lowercase “s” indicates sharp notes and students’ capacity for independent study. Figure 8 elaborates chords in musical notation. To denote flat notes or chords, b on the dataset’s learning activity ratio (%) [26]. As obtained in equation (10) learning activity has been is used in lower case. *is document contains a list of chord placements and discussed. *is paper presents the fundamental methodol- ogy of conventional piano instruction and investigates the chords. It is organized into four columns: Chord, Note1, Note2, and Note3. A string that includes the name of the use of multimedia technologies in the context of piano International Transactions on Electrical Energy Systems 11 Carefully read the requirements of the intelligent network piano class and clarify the purpose of the experiment Practice piano playing Use the system boot Wearable system interface to enter the according to the equipment course requirements virtual space scene Take off system equipment End of piano lesson Figure 6: Student piano lesson for student learning performance analysis. 10 20 30 40 50 10 20 30 40 50 Number of students Number of Students JAVA-PTMS IPINTSM JAVA-PTMS IPINTSM BPTM MPTM BPTM MPTM PTS PTS Figure 8: Learning activity ratio (%). Figure 7: Learning skills analysis ratio (%). instruction. is article presents a design for integrating the student modelling module to gather historical data on multimedia technologies into piano instruction. In con- their music listening habits. Piano music education ideals clusion, the research discusses the advancements that have and nonutilitarian music values inƒuence people’s behavior. been made in the eld of multimedia piano instruction and Students’ nonverbal cues are usually useful to piano in- expresses the hope that circumstances may be established to structors in gauging their level of commitment. e length of facilitate the seamless integration of multimedia technology time students spend engaged in a task, the quality of the and the activities involved in piano instruction. work they produce, and the way they communicate their feelings and ideas are all behavioral indications of motiva- tion. Figure 9 depicts the dataset’s learning behavior analysis 4.1.3. Learning Behavior Analysis (%). Neural networks are ratio (%) based on dataset [26]. widely used in piano playing and education. A neural As initialized in equation (12), learning behavior has network-based recommendation model is the focus of this been calculated. Using the preprocessed performance ma- study, which focuses on piano performance and training terial and spectral properties extracted from the spectrum by systems that are based on music content and user history. To the neural network may be trained to provide a regression build a user preference feature model, students often utilize model that can be used to predict piano performance Learning Skills Analysis (%) Learning Activity Analysis (%) 12 International Transactions on Electrical Energy Systems 10 20 30 40 50 Number of Students 10 20 30 40 50 Number of Students JAVA-PTMS IPINTSM JAVA-PTMS IPINTSM BPTM MPTM BPTM MPTM PTS PTS Figure 10: Student performance ratio (%). Figure 9: Learning behavior analysis ratio (%). 4.1.5. Teaching Evaluation Analysis (%). Traditional characteristics. Recommended music items are generated by methods of instruction often result in students’ lack of combining the regression model’s predictions of piano initiative when studying the piano. e students’ lack of performance attributes with a user’s stated preferences and interest in studying leads to fewer attempts to master the then using the recommendation algorithm module to build a piano and slows overall progress in their professional suc- list of music items in which the user would be interested. cesses. Students’ learning excitement may be considerably boosted by the incorporation and direction of multimedia technology and students’ cognitive abilities can be improved 4.1.4. Student Performance Analysis (%). Learning the piano while they are learning to play the piano. Figure 11 explores is not a one-time activity, and it often demands students to the dataset’s teaching evaluation analysis (%) [26]. have adequate knowledge and tracking complete recognition See equation (4) for output layer node outcome calcu- of the piano learners should need to have a specic skill play lated for teaching evaluation ratio, as well as student’s to achieve an overall performance level. e use of multi- performance. Teaching can be made easier with the help of media technology enables teachers to provide students with multimedia technology, which makes it possible to simplify a reasonably open educational environment and space where the teaching process. Some of the more abstract aspects of students will thoroughly understand piano knowledge. is piano playing and future technologies can be shown through positively impacts the composition of emotional resonance the visual display provided by multimedia, which helps to and energy-boosting exchange, which in turn creates con- prevent the issue of false cognition on the part of students as ditions for learners to enhance plenty of vitality for learning well as the preconceptions that can get in the way of im- and performance ability. Figure 10 elaborates on the student proving the eˆectiveness of piano learning. Table 1 shows the performance ratio (%) based on dataset [26]. results and discussion outcome. In Polyhymnia, a piano performance may be automated. An expressive performance may be guided by musical sig- nals, which can be interpreted in many diˆerent ways. 5. Discussion Because of this, we present adaptable parametric models that can be automatically interpreted. Expressive piano playing Using multimedia technology, this study explores the relies heavily on its polyphonic characteristics. Polyphony is concepts and characteristics of multimedia network teaching common in piano compositions. is article will discuss technology, outlining the components, classications, cur- them and refer to them as polyphonic expressiveness. rent state of development, and potential future applications. Performances with polyphonic expressiveness were found to Two features of the teaching content structure and content sound better than those without it, using a statistical model delivery applications illustrate the key technologies of polyphonic piano renditions. Based on the present state of employed in designing the network music education system. multimedia technology application in the design of inte- e many subsystems of the streaming teaching system are gration in piano instruction, this article focuses primarily on logically organized, and numerous sorts of technological the application structure and general design of multimedia challenges that must be overcome when using multimedia technologies in piano education. It concludes that multi- are examined. is paper proposes a system of networked media piano education is innovative and groundbreaking, multimedia instruction. To deliver educational services to and the article intends to establish the circumstances for its faculty, students system uses existing campus network in- smooth growth. frastructure software exclusively. is study’s integration of Learning Behavior Analysis (%) Student Performance Ratio (%) International Transactions on Electrical Energy Systems 13 References [1] L. Li, “Study on the innovation of piano teaching in normal colleges and universities,” Creative Education, vol. 09, no. 05, pp. 697–701, 2018. [2] L. X. Zhang, “e “four main factors theory” of piano teaching and its systematic thinking,” Advances in Social Science Ed- ucation and Humanities Research, vol. 322, pp. 359–362, 2019. [3] G. Comeau, Y. Lu, and M. Swirp, “On-site and distance piano teaching: an analysis of verbal and physical behaviours in a 50 teacher, student and parent,” Journal of Music, Technology and Education, vol. 12, no. 1, pp. 49–77, 2019. [4] Z. Ye Yang, “Modern piano teaching technologies: accessi- bility, eˆectiveness, the need for pedagogues,” Ilkogretim 10 20 30 40 50 Online, vol. 19, no. 3, pp. 1812–1819, 2020. Number of Students [5] J. Li, “Analysis of piano curriculum education and cultivation JAVA-PTMS IPINTSM of creative thinking ability,” Region - Educational Research and Reviews, vol. 2, no. 1, p. 6, 2020. BPTM MPTM [6] N. Lu and L. Dong, “e signicance and application of PTS Chinese piano works in piano teaching in colleges and uni- versities,” Frontiers in Educational Research, vol. 2, no. 4, Figure 11: Teaching evaluation analysis (%). [7] W. Qianfang, “Piano teaching in colleges and universities in Table 1: Results and discussion outcome. the environment of new media,” Higher Education and Oriental Studies, vol. 2, no. 3, 2022. Number of performance metrics Outcome (%) [8] Y. Wang, “Optimization of the music teaching management Learning skills ratio 97.6 system based on emotion recognition,” Computational In- Learning activity ratio 98.5 telligence and Neuroscience, vol. 2022, Article ID 4568041, 9 Learning behavior ratio 94.2 pages, 2022. Student performance ratio 93.8 [9] W. Ma, “Comprehensive evaluation system for realizing the Teaching evaluation ratio 90.3 ability of university piano teaching,” Mobile Information Systems, vol. 2022, Article ID 6895484, 8 pages, 2022. multimedia network technology and classroom instruction [10] D. Lu, “Inheritance and promotion of Chinese traditional may increase the teaching impact and provide a pleasant music culture in college piano education,” Heritage Science, teaching environment that encourages students to learn vol. 10, no. 1, pp. 75–10, 2022. independently and collaborate. ere are three levels in [11] Z. Chen, “Rethinking the university of the piano teaching which this article’s di culties may be found: instructors, model in the Internet era,” Frontiers in Art Research, vol. 4, students, and school administration. As a rst step, it is no. 3, 2022. [12] D. Wang, “Analysis of multimedia teaching path of popular recommended that educators thoroughly understand how music based on multiple intelligence teaching mode,” Ad- multimedia networks may be used to educate students and vances in Multimedia, vol. 2022, Article ID 7166569, 10 pages, assemble a team capable of overseeing the implementation of these networks in the classroom. e use of multimedia [13] C. Li, “A deep learning-based piano music notation recog- technology in piano instruction may help students overcome nition method,” Computational Intelligence and Neuroscience, limitations of time and distance, allowing them to get a vol. 2022, Article ID 2278683, 9 pages, 2022. deeper understanding of the art of piano playing and the [14] W. Gu, “Recognition algorithm of piano playing music in information associated with it. e experimental results intelligent background,” Mobile Information Systems, show that the proposed MPTM achieves the learning skills vol. 2022, Article ID 1245078, 11 pages, 2022. ratio of 97.6%, learning activity ratio of 98.5%, student [15] K. Lei, “e eˆectiveness of special apps for online piano performance ratio of 93.8%, teaching evaluation ratio of lessons,” Interactive Learning Environments, pp. 1–12, 2022. 90.3%, and learning behavior ratio of 94.2% when compared [16] X. Liu, “Research on piano performance optimization based on big data and bp neural network technology,” Computa- to other methods. tional Intelligence and Neuroscience, vol. 2022, Article ID 1268303, 10 pages, 2022. Data Availability [17] F. Wang, “e eˆect of multimedia teaching model of music course in colleges and universities based on classroom Audio e data that support the ndings of this study are available data mining technology,” Tobacco Regulatory Science, vol. 7, from the corresponding author upon reasonable request. no. 5, pp. 4520–4531, 2021. [18] Z. Nie, “Design and implementation of JAVA-based piano Conflicts of Interest teaching management system,” Educational Sciences: ’eory & Practice, vol. 18, no. 5, 2018. e authors declare no potential conƒicts of interest with [19] L. Changhan, A. Cleesuntorn, and S. Phongsatha, “A model of respect to the research, authorship, and/or publication of digital piano training system to improve the comprehensive this article. performance of pre-school education major sudents: a case Teaching Evaluation Analaysis (%) 14 International Transactions on Electrical Energy Systems study at a public university in Hunan, China,” AU-GSB e-Journal, vol. 13, no. 2, pp. 49–56, 2020. [20] L. Zhu, T. Phongsatha, and A. Intravisit, “A blended piano teaching model for non-piano music major students in Hunan city University,” AU-GSB e-Journal, vol. 13, no. 2, pp. 38–48, [21] A. Yonathan, “Organic concept in rolf-dieter arens’s piano teaching strategy,” Malaysian Journal of Music, vol. 11, no. 1, pp. 1–13, 2022. [22] C. Shuo and C. Xiao, “*e construction of internet+ piano intelligent network teaching system model,” Journal of In- telligent & Fuzzy Systems, vol. 37, no. 5, pp. 5819–5827, 2019. [23] L. Unlu, ¨ “A qualitative study of the perspectives of music students on distance piano education,” Education Quarterly Reviews, vol. 5, no. 1, 2022. [24] J. Fu, “Analysis on the piano teaching mode of flipped class in higher education,” Frontiers in Educational Research, vol. 3, no. 14, 2020. [25] https://www.kaggle.com/datasets/davidbroberts/piano-triads- wavset.

Journal

International Transactions on Electrical Energy SystemsHindawi Publishing Corporation

Published: Aug 29, 2022

References