Speaker extraction using LCMV beamformer with DNN-based SPP and RTF identification scheme
Ariel Malek, Shlomo E. Chazan, Ilan Malka, Vladimir Tourbabin, Jacob H. Goldberger, Eli Tzirkel-Hancock, Sharon Gannot · 2017
The linearly constrained minimum variance (LCMV)-beamformer (BF) is a viable solution for desired source extraction from a mixture of speakers in a noisy environment. The performance in terms of speech distortion, interference cancellation and noise reduction depends on the estimation of a set of parameters. This paper presents a new mechanism to update the parameters of the LCMV-BF. A new speech presence probability (SPP)-based voice activity detector (VAD) controls the noise covariance matrix update, and a speaker position identifier (SPI) procedure controls the relative transfer functions (RTFs) update. A postfilter is then applied to the BF output to further attenuate the residual noise signal. A series of experiments using real-life recordings confirm the speech enhancement capabilities of the proposed algorithm.