This chapter addresses a blind sparse source separation method that can employ arbitrarily arranged multiple microphones. Some sparse source separation methods, which rely on source sparseness and an anechoic mixing model, have already been proposed. The validity of the sparseness and anechoic assumptions will be investigated in this chapter. As most of the existing methods utilize a stereo (two sensors) system, they limit the separation ability to a 2-dimensional half-plane. This chapter describes a method for multiple microphones. This method employs the k-means algorithm, which is an efficient clustering algorithm. The method can be easily applied to three or more sensors arranged nonlinearly. Promising results were obtained for 2- and 3-dimensionally distributed speech signals with nonlinear/nonuniform sensor arrays in a real room even in underdetermined situations.
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Araki, S., Sawada, H., Makino, S. (2007). K-means Based Underdetermined Blind Speech Separation. In: Makino, S., Sawada, H., Lee, TW. (eds) Blind Speech Separation. Signals and Communication Technology. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6479-1_9
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