Sources separation of mono signal based on convolutive NMF

In many applications such as music transcription, audio forensics, speech denoising, it is needed to decompose a mono recording into its respective sources. These techniques are usually referred to as blind source separation (BSS). Recently, one of the techniques used in BSS is non-negative matrix factorization (NMF) both in supervised and unsupervised learning method. In this paper we focus on convolutive NMF algorithms to evaluate the performance of BSS in which supervised mode is used. The results on music mixtures of the MASS database based on signal to distortion ratio (SDR) and signal to artefact ratio (SAR) show that the proposed system perform a good reconstruction of sources signal.