Multi-microphone noise reduction techniques as front-end devices for speech recognition
Speech Communication - Special issue on noise robust ASR
QRD-based unconstrained optimal filtering for acoustic noise reduction
Signal Processing
GSVD-based optimal filtering for single and multimicrophone speech enhancement
IEEE Transactions on Signal Processing
Multichannel post-filtering in nonstationary noise environments
IEEE Transactions on Signal Processing
New insights into the noise reduction Wiener filter
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, frequency domain multi-channel noise reduction algorithms are proposed, based on the subspace decomposition of narrow-band spatial covariance matrices. In speech-present periods, the multi-channel input signals are decomposed into speech and noise spatial subspaces. The noise eigenvalues are modified in order to update the noise statistics not only in the noise-only period but also in the speech-present period. Three approaches are introduced for the noise eigenvalue modification, which are based on the rank-1 property of the speech narrow-band spatial covariance matrix for the single speech source. The proposed algorithms are tested with the simulated data and real data, and the results show that the proposed methods yield better performance compared to the conventional multi-channel Wiener filtering and the time domain subspace approaches.