Parallel Structured Independent Component Analysis for SIMO-Model-Based Blind Separation and Deconvolution of Convolutive Speech Mixture

IJCNN2003: IEEE International Joint Conference on Neural Networks, July 20-24, 2003, Portland, Oregon, USA.We propose a two-stage blind separation and deconvolution (BSD) algorithm for a convolutive mixture of temporally correlated signals, in which a new single-input multiple-output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under fidelity control of the entire separation system. 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. After the separation by SIMO-ICA, a simple blind deconvolution technique based on multichannel inverse filtering for the SIMO model can be applied even when the mixing system is the nonminimum phase system and each source signal is temporally correlated. The experimental results obtained under the reverberant condition reveal that the sound quality of the separated signals in the proposed method is superior to that in the conventional ICA-based BSD