Blind speech dereverberation using sparse decomposition and multi-channel linear prediction

Abstract

In this study, a blind speech dereverberation method in a noiseless single input multiple output acoustic channel is proposed. The method is based on multichannel linear prediction (MCLP) in STFT domain assuming sparsity in both residual speech and channel coefficients. The proposed speech dereverberation algorithm assumes that both the residual speech signal and the linear prediction coefficients is sparse. The optimization was performed by convex optimization using ADMM and CVX. The proposed model was compared with state of the art methods with lp norm optimization criteria. Simulations were evaluated in different room models with various reverberation times, numbers of microphones and parameter adjustments. The results show that the performance of the proposed method is superior in terms of speech dereverberation assessment criteria.