Effective speech dereverberation is a prerequisite in such applications as hands-free telephony, voice-based human-machine interfaces, and hearing aids. Blind multichannel speech dereverberation methods based on multichannel linear prediction (MCLP) can estimate the dereverberated speech component without any knowledge of the room acoustics. This can be achieved by estimating and subtracting the undesired reverberant component from the reference microphone signal. This report presents a general framework that exploits sparsity in the time–frequency domain of a MCLP-based speech dereverberation. The framework combines a wideband or a narrowband signal model with either an analysis or a synthesis sparsity prior, and generalizes state-of-the-art MCLP-based speech dereverberation methods.
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