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Itakura-Saito Divergence Non Negative Matrix Factorization with Application to Monaural Speech Separation

by A. Adewusi, K. A. Amusa, A. R. Zubair
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 153 - Number 9
Year of Publication: 2016
Authors: A. Adewusi, K. A. Amusa, A. R. Zubair
10.5120/ijca2016912112

A. Adewusi, K. A. Amusa, A. R. Zubair . Itakura-Saito Divergence Non Negative Matrix Factorization with Application to Monaural Speech Separation. International Journal of Computer Applications. 153, 9 ( Nov 2016), 17-22. DOI=10.5120/ijca2016912112

@article{ 10.5120/ijca2016912112,
author = { A. Adewusi, K. A. Amusa, A. R. Zubair },
title = { Itakura-Saito Divergence Non Negative Matrix Factorization with Application to Monaural Speech Separation },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 153 },
number = { 9 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume153/number9/26431-2016912112/ },
doi = { 10.5120/ijca2016912112 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:58:41.047906+05:30
%A A. Adewusi
%A K. A. Amusa
%A A. R. Zubair
%T Itakura-Saito Divergence Non Negative Matrix Factorization with Application to Monaural Speech Separation
%J International Journal of Computer Applications
%@ 0975-8887
%V 153
%N 9
%P 17-22
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Monaural source separation is an interesting area that has received much attention in the signal processing community as it is a pre-processing step in many applications. However, many solutions have been developed to achieve clean separation based on Non-Negative Matrix Factorization (NMF). In this work, we proposed a variant of Itakura-Saito Divergence NMF based on source filter model that captures the temporal continuity of speech signal. The algorithm shows a very good separation results for mixture of two speech sources in terms of artifacts reduction. Besides that, Source to distortion ratio (SDR) and Source to Artifact Ratio (SAR) were found to be higher when compared with NMF algorithms with Kullback-Leibler and Euclidean divergences.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Itakura-Saito divergence monaural source separation Non Negative Matrix Factorization

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