Sparse coding for convolutive blind audio source separation

Authors:
Maria G. Jafari;Samer A. Abdallah;Mark D. Plumbley;Mike E. Davies
Affiliations:
Centre for Digital Music, Queen Mary University of London, UK;Centre for Digital Music, Queen Mary University of London, UK;Centre for Digital Music, Queen Mary University of London, UK;Centre for Digital Music, Queen Mary University of London, UK
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 3
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Abstract

In this paper, we address the convolutive blind source separation (BSS) problem with a sparse independent component analysis (ICA) method, which uses ICA to find a set of basis vectors from the observed data, followed by clustering to identify the original sources. We show that, thanks to the temporally localised basis vectors that result, phase information is easily exploited to determine the clusters, using an unsupervised clustering method. Experimental results show that good performance is obtained with the proposed approach, even for short basis vectors.