TY - GEN
T1 - A Bilinear Framework For Adaptive Speech Dereverberation Combining Beamforming And Linear Prediction
AU - Yang, Wenxing
AU - Huang, Gongping
AU - Brendel, Andreas
AU - Chen, Jingdong
AU - Benesty, Jacob
AU - Kellermann, Walter
AU - Cohen, Israel
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Speech dereverberation algorithms based on multichannel linear prediction (MCLP) are effective under various acoustic conditions. This paper proposes a bilinear form for the MCLP based dereverberation, where the MCLP filter is expressed as a Kronecker product of a spatial filter and a temporal filter. Then, a recursive least-squares (RLS)-based algorithm is derived for adaptive speech dereverberation. Compared with the original MCLP-based adaptive algorithm, the advantages of the proposed method are twofold: (1) the computational complexity is significantly reduced and is more suitable for dynamic scenarios, since fewer parameters have to be estimated per signal-block observation; and (2) it is more robust to noise by optimizing the spatial filter as a weighted minimum power distortionless response (wMPDR) beamformer. Simulation results validate the advantages of the proposed algorithm.
AB - Speech dereverberation algorithms based on multichannel linear prediction (MCLP) are effective under various acoustic conditions. This paper proposes a bilinear form for the MCLP based dereverberation, where the MCLP filter is expressed as a Kronecker product of a spatial filter and a temporal filter. Then, a recursive least-squares (RLS)-based algorithm is derived for adaptive speech dereverberation. Compared with the original MCLP-based adaptive algorithm, the advantages of the proposed method are twofold: (1) the computational complexity is significantly reduced and is more suitable for dynamic scenarios, since fewer parameters have to be estimated per signal-block observation; and (2) it is more robust to noise by optimizing the spatial filter as a weighted minimum power distortionless response (wMPDR) beamformer. Simulation results validate the advantages of the proposed algorithm.
KW - beamforming
KW - Dereverberation
KW - Kronecker product filtering
KW - multichannel linear prediction
KW - recursive least-squares (RLS) algorithm
UR - http://www.scopus.com/inward/record.url?scp=85141352217&partnerID=8YFLogxK
U2 - 10.1109/IWAENC53105.2022.9914728
DO - 10.1109/IWAENC53105.2022.9914728
M3 - 会议稿件
AN - SCOPUS:85141352217
T3 - International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings
BT - International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Workshop on Acoustic Signal Enhancement, IWAENC 2022
Y2 - 5 September 2022 through 8 September 2022
ER -