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.
This work was funded by EPSRC grants GR/S85900/01, GR/R54620/01, and GR/S82213/01.
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Jafari, M.G., Abdallah, S.A., Plumbley, M.D., Davies, M.E. (2006). Sparse Coding for Convolutive Blind Audio Source Separation. In: Rosca, J., Erdogmus, D., PrÃncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_17
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DOI: https://doi.org/10.1007/11679363_17
Publisher Name: Springer, Berlin, Heidelberg
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