This paper addresses the problem of extracting a desired speech source from a multispeaker environment in the presence of background noise. A new adaptive beamforming structure is proposed for this speech enhancement problem. This structure incorporates power spectral density (PSD) estimation of the speech sources together with a noise statistics update. An inactive-source detector based on minimum statistics is developed to detect the speech presence and to track the noise statistics. Performance of the proposed beamformer is investigated and compared to the minimum variance distortionless response (MVDR) beamformer with or without a postfilter in a real hands-free communication environment. Evaluations show that the proposed beamformer offers good interference and noise suppression levels while maintaining low distortion of the desired source.

This paper presents a new space constrained adaptive beamformer employing an updated source power spectral density (PSD). The space constraints are used to capture the target signal spatially and to provide robustness against steering error vectors. The PSD update on the other hand ensures that the spectral information of the desired source is reflected continuously on the space constraints. As such, target signal extraction can be achieved with minimum distortion. The beamformer operates in a subband structure to allow time-frequency operation for each channel, yielding a combination of weighted spatial and temporal filters. Evaluations on real car data show that the proposed algorithm significantly improves the speech intelligibility with noise suppression level up to 21 dB.