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An automatic approach of audio feature engineering for the extraction, analysis and selection of descriptors

An automatic approach of audio feature engineering for the extraction, analysis and selection of... Currently, it is critical to find the correct features from the audio, in order to analyze the information contained in it. This paper analyzes several feature types in audio from different points of view: time series, sound engineering, etc. In particular, the description of audio as a set of time series is not very common in the literature, and it is one of the aspects studied in this paper. Particularly, this paper proposes an automated method for feature engineering in audios, to extract, analyze and select the best features in a given context. Specifically, this paper develops a hybrid scheme of extraction of audio descriptors based on different principles and defines an automatic approach for the analysis and selection of these descriptors in a given audio context. Finally, our approach was tested on grouping tasks and compared to previous works on audio classification problems, with encouraging results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

An automatic approach of audio feature engineering for the extraction, analysis and selection of descriptors

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-020-00202-1
Publisher site
See Article on Publisher Site

Abstract

Currently, it is critical to find the correct features from the audio, in order to analyze the information contained in it. This paper analyzes several feature types in audio from different points of view: time series, sound engineering, etc. In particular, the description of audio as a set of time series is not very common in the literature, and it is one of the aspects studied in this paper. Particularly, this paper proposes an automated method for feature engineering in audios, to extract, analyze and select the best features in a given context. Specifically, this paper develops a hybrid scheme of extraction of audio descriptors based on different principles and defines an automatic approach for the analysis and selection of these descriptors in a given audio context. Finally, our approach was tested on grouping tasks and compared to previous works on audio classification problems, with encouraging results.

Journal

International Journal of Multimedia Information Retrieval – Springer Journals

Published: Jan 7, 2021

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