International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 153 - Issue 9 |
Published: Nov 2016 |
Authors: A. Adewusi, K. A. Amusa, A. R. Zubair |
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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 }, year = { 2016 }, volume = { 153 }, number = { 9 }, pages = { 17-22 }, doi = { 10.5120/ijca2016912112 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2016 %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%T %J International Journal of Computer Applications %V 153 %N 9 %P 17-22 %R 10.5120/ijca2016912112 %I Foundation of Computer Science (FCS), NY, USA
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.