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 - Funding Information: This work was supported in part by the National Key Research and Development Program of China under Grant No. 2018AAA0102200 and in part by the Key Program of National Science Foundation of China (NSFC) under Grants 61831019 and 62192713. 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 - Dereverberation
KW - Kronecker product filtering
KW - beamforming
KW - multichannel linear prediction
KW - recursive least-squares (RLS) algorithm
UR - https://www.scopus.com/pages/publications/85141352217
U2 - 10.1109/IWAENC53105.2022.9914728
DO - 10.1109/IWAENC53105.2022.9914728
M3 - Conference contribution
T3 - International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings
BT - International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings
T2 - 17th International Workshop on Acoustic Signal Enhancement, IWAENC 2022
Y2 - 5 September 2022 through 8 September 2022
ER -