Blind Speech Deconvolution via Pretrained Polynomial Dictionary and Sparse Representation

Blind speech deconvolution aims to estimate both the source speech and acoustic channel from a reverberant speech signal. The problem is ill-posed and underdetermined, which often requires prior knowledge for the estimation of the source and channel. In this paper, we propose a blind speech deconvolution method via a pretrained polynomial dictionary and sparse representation. A polynomial dictionary learning technique is employed to train the dictionary from room impulse responses, which is then used as prior information to estimate the source and the acoustic impulse responses via an alternating optimization strategy. Simulations are provided to demonstrate the performance of the proposed method.