Online blind source separation based on time-frequency sparseness

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Abstract
Recently, blind source separation (BSS) has been proposed to separate signals recorded by a microphone array in a reverberant environment. This paper deals with BSS of a time-varying number of moving sources, which often occurs in practical situations. We develop two online algorithms based on time-frequency (TF) sparseness that are able to deal with moving sources: A block online algorithm that estimates the number of sources and a gradient based online algorithm with prespecified maximum number of sources. Both algorithms are evaluated in simulations and real-world scenarios and show good separation performance.