The problem of blind source separation (BSS) of convolved acoustic signals is of great interest for many classes of\r\napplications. Due to the convolutive mixing process, the source separation is performed in the frequency domain,\r\nusing independent component analysis (ICA). However, frequency domain BSS involves several major problems\r\nthat must be solved. One of these is the permutation problem. The permutation ambiguity of ICA needs to be\r\nresolved so that each separated signal contains the frequency components of only one source signal. This article\r\npresents a class of methods for solving the permutation problem based on information theoretic distance\r\nmeasures. The proposed algorithms have been tested on different real-room speech mixtures with different\r\nreverberation times in conjunction with different ICA algorithms.
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