Clustering NMF basis functions using Shifted NMF for monaural sound source separation

Non-negative Matrix Factorization (NMF) has found use in single channel separation of audio signals, as it gives a parts-based decomposition of audio spectrograms where the parts typically correspond to individual notes or chords. However, a notable shortcoming of NMF is the need to cluster the basis functions to their sources after decomposition. Despite recent improvements in algorithms for clustering the basis functions to sources, much work still remains to further improve these algorithms. To this end we present a novel clustering algorithm which overcomes some of the limitations of previous clustering methods. This involves the use of Shifted Nonnegative Matrix Factorization (SNMF) as a means of clustering the frequency basis functions obtained from NMF. Results show that this gives improved clustering of pitched basis functions over previous methods.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Rajesh Jaiswal, Derry Fitzgerald, Dan Barry, Eugene Coyle, Scott Rickard
• Keywords: Basis Functions, ICASSP 2011, Matrix Factorization, Non-Negative Matrix Factorization, Signal Processing