Online blind speech extraction based on a locally quadratic kurtosis criteria and a preprocessing Automatic Gain Controller

This paper focuses on realtime speech extraction using blind adaptive beamforming. The speech extraction is carried out using an approximation of the kurtosis measure in a subband domain. The introduced kurtosis approximation is an improvement of a recently proposed approximation technique where a locally quadratic crite rion was solved at each iteration. The improvement introduced in this paper regards an approach to normalize this same criterion using a pre-processing Automatic Gain Control unit, and thereby making the algorithm invariant to input signal scales. The proposed method outperforms the recent technique in terms of Signal to Interference Ratio improvement. In addition, the increased memory consumption and processing load due to the proposed improvement is comparably low and this is often desirable in a realtime Digital Signal Processor (DSP) implementation. Further, a real-time implementation of the method is conducted and results with real data is presented.