BLIND SIGNAL SEPARATION OF AUDIO SIGNALS

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This paper reviews a real-time blind source separation (BSS) method for convolutive mixtures of audio signals, in which a single-input multiple-output(SIMO)-model-based independent component analysis(ICA) and a new 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, novel 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 develop a pocket-size real-time DSP module implementing the new BSS method, and report the experimental evaluation of the proposed method's superiority to the conventional BSS methods, regarding moving-sound separation.

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