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

A. Adewusi, K. A., A. R.
2016 International Journal of Computer Applications  
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
more » ... 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. General Terms Signal processing.
doi:10.5120/ijca2016912112 fatcat:tiu2j4lz7rbxnnox625xbxg5iu