TY - GEN
T1 - Constrained Wiener gains and filters for single-channel and multichannel noise reduction
AU - Long, Tao
AU - Benesty, Jacob
AU - Chen, Jingdong
N1 - Publisher Copyright:
© 2016 Asia Pacific Signal and Information Processing Association.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - Noise reduction has long been an active research topic in signal processing and many algorithms have been developed over the last four decades. These algorithms were proved to be successful in some degree to improve the signal-to-noise ratio (SNR) and speech quality. However, there is one problem common to all these algorithms: the volume of the enhanced signal after noise reduction is often perceived lower than that of the original signal. This phenomenon is particularly serious when SNR is low. In this paper, we develop two constrained Wiener gains and filters for noise reduction in the short-time Fourier transform (STFT) domain. These Wiener gains and filters are deduced by minimizing the mean-squared error (MSE) between the clean speech and the speech estimate with the constraint that the sum of the variances of the filtered speech and residual noise is equal to the variance of the noisy observation.
AB - Noise reduction has long been an active research topic in signal processing and many algorithms have been developed over the last four decades. These algorithms were proved to be successful in some degree to improve the signal-to-noise ratio (SNR) and speech quality. However, there is one problem common to all these algorithms: the volume of the enhanced signal after noise reduction is often perceived lower than that of the original signal. This phenomenon is particularly serious when SNR is low. In this paper, we develop two constrained Wiener gains and filters for noise reduction in the short-time Fourier transform (STFT) domain. These Wiener gains and filters are deduced by minimizing the mean-squared error (MSE) between the clean speech and the speech estimate with the constraint that the sum of the variances of the filtered speech and residual noise is equal to the variance of the noisy observation.
UR - http://www.scopus.com/inward/record.url?scp=85013796857&partnerID=8YFLogxK
U2 - 10.1109/APSIPA.2016.7820804
DO - 10.1109/APSIPA.2016.7820804
M3 - 会议稿件
AN - SCOPUS:85013796857
T3 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
BT - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Y2 - 13 December 2016 through 16 December 2016
ER -