Blind source separation combining SIMO-model-based ICA and adaptive beamforming

A new two-stage blind source separation (BSS) for convolutive mixtures of speech is proposed, in which a single-input multiple-output-model-based ICA (SIMO-ICA) and an adaptive beamforming (ABF) are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO model-based signals from independent sources as they are at the microphones. Thus, the separated signals of SIMO-ICA can maintain the spatial qualities of each sound source, and the directions-of-arrival (DOAs) of the sources can be estimated after the separation by SIMO-ICA. Owing to the attractive property, the supervised ABF can be applied to removing the residual interference components efficiently after the SIMO-ICA and DOA estimation procedures. Experimental results reveal that separation performance can be considerably improved by using the proposed method. In addition, the proposed method outperforms the combination of the conventional SIMO-output-type ICA and ABF, as well as both the simple ICA and the simple ABF.