Blind source separation based on subband ICA and beamforming

This paper describes a new blind source separation (BSS) method on microphone array using the subband independent component analysis (ICA) and beamforming. The proposed array system consists of the following three sections: (1) subband-ICA-based BSS section, (2) null beamforming section, and (3) integration of (1) and (2) based on the algorithm diversity. Using this technique, we can resolve the low-convergence problem on optimization in ICA. Signal separation and speech recognition experiments clarify that the noise reduction rate (NRR) of about 18 dB is obtained under the nonreverberant condition, and NRRs of 8 dB and 6 dB are obtained in the case that the reverberation times are 150 msec and 300 msec. These performances are superior to those of both simple ICA-based BSS and simple beamforming method. Also, the improvements of the proposed method in word recognition rates are superior to those of the conventional ICA-based BSS method under all reverberant conditions.