Covariance matrix reconstruction for speech separation and denoising in diffuse noise

In order to improve the performance of multichannel speech separation algorithm in diffuse noise,a spatial covariance model and parameter estimation method for speech separation and noise reduction are proposed.In this method,diffuse noise is assumed to be an independent source,and the spatial characteristics of the target source are modeled by the spatial covariance matrix reconstructed from the steering vector,and the multichannel Wiener filter for speech separation is estimated by the spatial covariance analysis method.Moreover,a joint parameter framework of this method and postfilter is proposed,which provides more compromise selections between speech dereverberation and noise reduction for the output signal.In the experiments of single-source and multi-source in diffuse noise,the proposed method outperformed the conventional methods in speech extraction and separation.The postfilter with joint parameters provides more satisfactory denoised speech.This verified the effectiveness of the proposed model and parameter estimation method.