Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Electrical and electronic engineering
Abstract
Non-negative Matrix Factorization (NMF) has found use in single
channel separation of audio signals, as it gives a parts-based decom-
position 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 af-
ter 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 Non-
negative 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
Recommended Citation
Jaiswal, R. et al. (2011) Clustering NMF Basis Functions Using Shifted NMF for Monaural Sound Source Separation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, 2011.
Funder
ABBEST Scholarship by DIT
Publication Details
Accepted for publication at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Prague, 2011