Blind Speech Separation in Presence of Correlated Noise with Generalized Eigenvector Beamforming

Konferenz: Sprachkommunikation 2008 - 8. ITG-Fachtagung
08.10.2008 - 10.10.2008 in Aachen, Germany

Tagungsband: Sprachkommunikation 2008

Seiten: 4Sprache: EnglischTyp: PDF

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Vu, Dang Hai Tran; Haeb-Umbach, Reinhold (Department of Communications Engineering, University of Paderborn)

This paper considers the convolutive blind source separation of speech sources in the presence of spatially correlated noise. We introduce a method for estimating the scaled mixing matrix from the sources to the microphones even if coherent noise is present. This is achieved by combining time-frequency sparseness with the generalized eigenvalue decomposition of the power spectral density matrix (PSD) of the noisy speech and noise-only microphone signals. Separation is performed by spatial filtering with coefficients constructed by Gram-Schmidt orthogonalization which places spatial nulls at the interferer’s direction. Experimental results show that our approach is capable of separating 2 sources in a reverberant environment (RT60=0ms..500ms) degraded by significant directional noise.