MLSP 2007 Data Analysis Competition: Two-Stage Blind Source Separation Combining SIMO-Model-Based ICA and Binary Masking

This paper reviews a real-time two-stage blind source separation (BSS) method for convolutive mixtures of speech, in which a single-input multiple-output (SIMO)-model-based independent component analysis (ICA) and a SIMO-model-based binary masking are combined. SIMO-model-based ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources in their original form at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to this attractive property, SIMO-model-based binary masking can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. In addition, the performance deterioration due to the latency problem in ICA can be mitigated by introducing real-time binary masking. We report the parameters used in MLSP 2007 data analysis, and the experimental evaluation of the proposed method's superiority to the conventional BSS methods, regarding static- and moving-sound separation.