Blind speech extraction combining ICA-based noise estimation and less-musical-noise nonlinear post processing

In this paper, we propose a new blind speech extraction microphone array combining an independent component analysis (ICA)-based noise estimator and nonlinear signal processing for achieving high-quality speech enhancement. The proposed method consists of three parts, namely, the ICA-based noise estimator for a robust target cancellation, channel-wise spectral subtraction (chSS), and post-beamforming to sum up the chSS outputs. We provide a detailed proof of the less musical noise generation property in the proposed method via higher-order statistics analysis, compared with the conventional multichannel speech enhancement methods. The superiority of the proposed method is assessed in the experimental subjective evaluation.