Generalized Eigenvector Blind Speech Separation Under Coherent Noise In A GSC Configuration

This Paper deals with a new Technique for multi-channel separation of speech signals from convolutive mixtures under coherent noise. We demonstrate how the scaled transfer functions from the sources to the microphones can be estimated even in the presence of stationary coherent noise. The key to this are generalized eigenvalue decompositions of the power spectral density (PSD) matrices of the noisy speech and noise-only microphone signals with a controlled estimation of these matrices exploiting time-frequency sparseness of the speech sources. Separation is further improved by subsequent Gram-Schmidt orthogonalization which places spatial nulls at the interferers{\rq} directions, while noise reduction is improved by employing a novel blocking matrix and adaptive interference canceller in a Generalized Sidelobe Canceller (GSC)-like structure. We report promising experimental results for 2 speech sources with significant coherent noise in reverberant environments (RT60=0oms..500ms).