TY - CHAP
T1 - Speech Enhancement in the STFT Domain
AU - Benesty, Jacob
AU - Chen, Jingdong
AU - Habets, Emanuël A.P.
N1 - Publisher Copyright:
The Author(s) 2012.
PY - 2012
Y1 - 2012
N2 - This work addresses this problem in the short-time Fourier transform (STFT) domain. We divide the general problem into five basic categories depending on the number of microphones being used and whether the interframe or interband correlation is considered. The first category deals with the single-channel problem where STFT coefficients at different frames and frequency bands are assumed to be independent. In this case, the noise reduction filter in each frequency band is basically a real gain. Since a gain does not improve the signal-to-noise ratio (SNR) for any given subband and frame, the noise reduction is basically achieved by liftering the subbands and frames that are less noisy while weighing down on those that are more noisy. The second category also concerns the single-channel problem. The difference is that now the interframe correlation is taken into account and a filter is applied in each subband instead of just a gain. The advantage of using the interframe correlation is that we can improve not only the long-time fullband SNR, but the frame-wise subband SNR as well. The third and fourth classes discuss the problem of multichannel noise reduction in the STFT domain with and without interframe correlation, respectively. In the last category, we consider the interband correlation in the design of the noise reduction filters. We illustrate the basic principle for the single-channel case as an example, while this concept can be generalized to other scenarios. In all categories, we propose different optimization cost functions from which we derive the optimal filters and we also define the performance measures that help analyzing them.
AB - This work addresses this problem in the short-time Fourier transform (STFT) domain. We divide the general problem into five basic categories depending on the number of microphones being used and whether the interframe or interband correlation is considered. The first category deals with the single-channel problem where STFT coefficients at different frames and frequency bands are assumed to be independent. In this case, the noise reduction filter in each frequency band is basically a real gain. Since a gain does not improve the signal-to-noise ratio (SNR) for any given subband and frame, the noise reduction is basically achieved by liftering the subbands and frames that are less noisy while weighing down on those that are more noisy. The second category also concerns the single-channel problem. The difference is that now the interframe correlation is taken into account and a filter is applied in each subband instead of just a gain. The advantage of using the interframe correlation is that we can improve not only the long-time fullband SNR, but the frame-wise subband SNR as well. The third and fourth classes discuss the problem of multichannel noise reduction in the STFT domain with and without interframe correlation, respectively. In the last category, we consider the interband correlation in the design of the noise reduction filters. We illustrate the basic principle for the single-channel case as an example, while this concept can be generalized to other scenarios. In all categories, we propose different optimization cost functions from which we derive the optimal filters and we also define the performance measures that help analyzing them.
KW - linearly constrained minimum variance (LCMV) filter
KW - maximum signal-to-noise ratio (SNR) filter
KW - microphone arrays
KW - minimum variance distortionless response (MVDR) filter
KW - prediction filter
KW - short-time Fourier transform (STFT) domain
KW - single-channel and multichannel
KW - Speech enhancement
KW - tradeoff filter
KW - Wiener filter
UR - http://www.scopus.com/inward/record.url?scp=105004724842&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23250-3
DO - 10.1007/978-3-642-23250-3
M3 - 章节
AN - SCOPUS:105004724842
T3 - SpringerBriefs in Speech Technology
SP - 1
EP - 106
BT - SpringerBriefs in Speech Technology
PB - Springer Science and Business Media B.V.
ER -