Séparation aveugle de sources sonores par factorization en matrices positives avec pénalité sur le volume du dictionnaire
Séparation aveugle de sources sonores par factorization en matrices positives avec pénalité sur le volume du dictionnaire
Audio source separation concerns techniques used to extract unknown signals called sources from a mixed signal. In this paper, we assume that the audio signal is recorded with a single microphone. Considering a mixed signal composed of various audio sources, the blind audio source separation consists in isolating and extracting each of the sources on the basis of a single recording. Usually, the only known information is the number of estimated sources present in the mixed signal. Based on a time-frequency representation of the signal, classical source separation techniques integrate algorithms such as nonnegative matrix factorization (NMF). Optimization problems in blind audio source separation are based on the minimization of criteria such as the Kullback-Leibler and Itakura-Saito divergences, both divergences belonging to the family of β-divergences. In this paper, we present a new model of separation based on the minimization of the Kullback-Leibler includinga penalty term promoting the columns of the dictionary matrix to have small volume. In order to solve this problem, the global cost function is replaced by a convex and separable auxiliary function that will be minimized. We will show that we obtain more interpretable results in the case where the factorization rank (that is, the number of sources present into the mixed signal) is overestimated.
Leplat, Valentin
019d30cb-499a-4996-967f-0d5566fcef56
Gillis, Nicolas
76af3b6e-6ece-4191-a229-a7ff3616915f
Siebert, Xavier
84432ab0-1b19-4056-860b-3ca29ee85db2
Ang, Man Shun
ed509ecd-39a3-4887-a709-339fdaded867
2019
Leplat, Valentin
019d30cb-499a-4996-967f-0d5566fcef56
Gillis, Nicolas
76af3b6e-6ece-4191-a229-a7ff3616915f
Siebert, Xavier
84432ab0-1b19-4056-860b-3ca29ee85db2
Ang, Man Shun
ed509ecd-39a3-4887-a709-339fdaded867
Leplat, Valentin, Gillis, Nicolas, Siebert, Xavier and Ang, Man Shun
(2019)
Séparation aveugle de sources sonores par factorization en matrices positives avec pénalité sur le volume du dictionnaire.
4 pp
.
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Conference or Workshop Item
(Paper)
Abstract
Audio source separation concerns techniques used to extract unknown signals called sources from a mixed signal. In this paper, we assume that the audio signal is recorded with a single microphone. Considering a mixed signal composed of various audio sources, the blind audio source separation consists in isolating and extracting each of the sources on the basis of a single recording. Usually, the only known information is the number of estimated sources present in the mixed signal. Based on a time-frequency representation of the signal, classical source separation techniques integrate algorithms such as nonnegative matrix factorization (NMF). Optimization problems in blind audio source separation are based on the minimization of criteria such as the Kullback-Leibler and Itakura-Saito divergences, both divergences belonging to the family of β-divergences. In this paper, we present a new model of separation based on the minimization of the Kullback-Leibler includinga penalty term promoting the columns of the dictionary matrix to have small volume. In order to solve this problem, the global cost function is replaced by a convex and separable auxiliary function that will be minimized. We will show that we obtain more interpretable results in the case where the factorization rank (that is, the number of sources present into the mixed signal) is overestimated.
Text
gretsifr_minVolNMF_Leplat (1)
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Published date: 2019
Alternative titles:
Blind separation of sound sources by factorization in positive matrices with penalty on the volume of the dictionary
Identifiers
Local EPrints ID: 497966
URI: http://eprints.soton.ac.uk/id/eprint/497966
PURE UUID: c0036bd6-8d71-4f27-9dba-fdb4b0617074
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Date deposited: 05 Feb 2025 17:46
Last modified: 22 Aug 2025 02:38
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Contributors
Author:
Valentin Leplat
Author:
Nicolas Gillis
Author:
Xavier Siebert
Author:
Man Shun Ang
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