EVALUATION OF FAST-CONVERGENCE ALGORITHM FOR ICA-BASED BLIND SOURCE SEPARATION OF REAL CONVOLUTIVE MIXTURE
European Signal Processing Conference(2002)
摘要
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 op- timization 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 optimiza- tion, 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 separa- tion performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.
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关键词
array signal processing,blind source separation,convergence of numerical methods,convolution,direction-of-arrival estimation,frequency-domain analysis,independent component analysis,iterative methods,matrix inversion,optimisation,DOA estimation,ICA-based blind source separation,beamforming,convolutive mixture,direction-of-arrival estimation,fast-convergence algorithm evaluation,frequency-domain ICA-based BSS method,independent component analysis,iterative optimization,mixing matrix inversion,reverberant condition,signal separation,temporal alternation,
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