A Novel Weighted Sparse Component Analysis for Underdetermined Blind Speech Separation

Sparse component analysis (SCA) is a popular underdetermined blind speech separation (UBSS) method. It models all sources to have an identical distribution. As speeches do not have identical distribution, SCA performs suboptimal. Some studies have improved the performance of SCA by weighting the sources through a reweighting scheme. However, they are not UBSS methods because they assume that the mixing process is known. This paper proposes a novel weighting scheme, called sparse spatial component analysis (SSCA) without the need to know the mixing process. In SSCA, weights, sources, and the parameters for modeling the mixing process are jointly optimized, making it a UBSS method. Simulation experiments show that for instantaneous mixtures, SSCA outperforms SCA and reweighted SCA, improving the source-to-distortion ratio (SDR) by 4 dB and reducing the computational time by 40%. Further, experiments using real-world recordings reveal that SSCA outperforms multichannel non-negative matrix factorization and full-rank covariance analysis (FCA) in terms of SDR. The speed of SSCA is 200% faster than FCA.