Blind Source Separation Combining Simo-Ica and Simo-Model-Based Binary Masking
Y. Mori, Tomoya Takatani, Hiroshi Saruwatari, Takashi Hiekata, T. Morita · 2006
A new two-stage blind source separation (BSS) for convolutive mixtures of speech is proposed, in which a single-input multiple-output (SIMO)-model-based ICA and a new SIMO-model-based binary mask processing 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 as they are at the microphones. Thus, the separated signals of SIMO-model-based ICA can maintain the spatial qualities of each sound source. Owing to the attractive property, novel SIMO-model-based binary mask processing can be applied to efficiently remove the residual interference components after SIMO-model-based ICA. The experimental results reveal that the separation performance can be considerably improved by using the proposed method compared with the conventional BSS methods