A method for filter equalization in convolutive blind source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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In this paper, we propose to use the scaling ambiguity of convolutive blind source separation for shortening the unmixing filters. An often used approach for separating convolutive mixtures is the transformation to the time-frequency domain where an instantaneous ICA algorithm can be applied for each frequency separately. This approach leads to the so called permutation and scaling ambiguity. While different methods for the permutation problem have been widely studied, the solution for the scaling problem is usually based on the minimal distortion principle. We propose an alternative approach that allows the unmixing filters to be as short as possible. Shorter unmixing filters will suffer less from circular-convolution effects that are inherent to unmixing approaches based on bin-wise ICA followed by permutation and scaling correction. The results for the new algorithm will be shown on a real-world example.