Recently, Non-negative Matrix Factorisation (NMF) has found application in separation of individual sound sources. NMF decomposes the spectrogram of an audio mixture into an additive parts based representation where the parts typically correspond to individual notes or chords. However, there is a need to cluster the NMF basis functions to their sources. Although, many attempts have been made to improve the clustering of the basis functions to sources, much research is still required in this area. Recently, Shifted Non-negative Matrix Factorisation (SNMF) was used to cluster these basis functions. To this end, we propose that the incorporation of group sparsity to the Shifted NMF based methods may benefit the clustering algorithms. We have tested this on SNMF algorithms with improved separation quality. Results show that this gives improved clustering of pitched basis functions over previous methods.

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Many sound source separation algorithms, such as NMF and related approaches, disregard phase information and operate only on magnitude or power spectrograms. In this context, generalised Wiener filters have been widely used to generate masks which are applied to the original complex-valued spectrogram before inversion to the time domain, as these masks have been shown to give good results. However, these masks may not be optimal from a perceptual point of view. To this end, we propose new families of masks and compare their performance to generalised Wiener filter masks using three different factorisation-based separation algorithms. Further, to-date no analysis of how the performance of masking varies with the number of iterations performed when estimating the separated sources. We perform such an analysis and show that when using these masks, running to convergence may not be required in order to obtain good separation performance.

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