Multi-channel noise reduction with beamforming and masking-based Wiener filtering for human-robot interface

摘要

In this paper, an efficient noise reduction algorithm is proposed for robust speech recognition. For the nonstationary noise reduction, frequency-domain beamforming-based speech enhancement is performed and masking-based Wiener filter is applied to the beamforming output. To design the masking-based Wiener filter, the spectrum of beamforming output is classified into noise spectrum and speech spectrum at each spectral bin by the inter-channel time delay between two reference inputs. Hamming windowing for the speech spectrum and noise spectrum is separately performed to smooth each spectrum. Then, the Wiener filtering is applied to the beamforming output. The performance of the proposed algorithm significantly improves the speech recognition accuracies and the signal-to-noise ratios.