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Sparse coding for convolutive blind audio source separation. (English) Zbl 1178.94078
Rosca, Justinian (ed.) et al., Independent component analysis and blind signal separation. 6th international conference, ICA 2006, Charleston, SC, USA, March 5–8, 2006. Proceedings. Berlin: Springer (ISBN 3-540-32630-8/pbk). Lecture Notes in Computer Science 3889, 132-139 (2006).
Summary: 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.
For the entire collection see [Zbl 1096.94002].

MSC:
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
68T05 Learning and adaptive systems in artificial intelligence
Software:
BSS Eval
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