Paper data
Title:
Evaluation of Fast-Convergence Algorithm for ICA-Based Blind Source Separation of Real Convolutive Mixture Author(s): Saruwatari Hiroshi, Graduate School of Information Science, Nara Institute of Science and Technology Kawamura Toshiya, Graduate School of Information Science, Nara Institute of Science and Technology Sawai Katsuyuki, Graduate School of Information Science, Nara Institute of Science and Technology Shikano Kiyohiro, Graduate School of Information Science, Nara Institute of Science and Technology Kaminuma Atsunobu, Nissan Research Center, NISSAN MOTOR CO., LTD. Sakata Masao, Nissan Research Center, NISSAN MOTOR CO., LTD. Page numbers in the proceedings: Volume I pp 551-554 Session: Echo Cancellation and Speech Enhancement
Paper abstract
We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation, (2) null beamforming based on the estimated DOA, and (3) integration of (1) and (2) based on the algorithm diversity in both iteration and frequency domain. The inverse of the mixing matrix obtained by ICA is temporally substituted by the matrix based on null beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.
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