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By Shoko Araki, Shoji Makino, Audrey Blin, Ryo Mukai and Hiroshi Sawada


We propose a method for separating speech signals with little distortion when the signals outnumber the sensors. Several methods have already been proposed for solving the underdetermined problem, and some of these utilize the sparseness of speech signals. These methods employ binary masks that extract a signal at time points where the number of active sources is estimated to be only one. However, these methods result in an unexpected excess of zeropadding and so the extracted speeches are severely distorted and have loud musical noise. In this paper, we propose combining a sparseness approach and independent component analysis (ICA). First, using sparseness, we estimate the time points when only one source is active. Then, we remove this single source from the observations and apply ICA to the remaining mixtures. Experimental results show that our proposed sparseness and ICA (SPICA) method can separate signals with little distortion even in a reverberant condition

Year: 2014
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