@inproceedings{f0441723c1b24cfb931f716d6dd50483,
title = "A Bilinear Framework For Adaptive Speech Dereverberation Combining Beamforming And Linear Prediction",
abstract = "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.",
keywords = "Dereverberation, Kronecker product filtering, beamforming, multichannel linear prediction, recursive least-squares (RLS) algorithm",
author = "Wenxing Yang and Gongping Huang and Andreas Brendel and Jingdong Chen and Jacob Benesty and Walter Kellermann and Israel Cohen",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 17th International Workshop on Acoustic Signal Enhancement, IWAENC 2022 ; Conference date: 05-09-2022 Through 08-09-2022",
year = "2022",
doi = "10.1109/IWAENC53105.2022.9914728",
language = "الإنجليزيّة",
series = "International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings",
booktitle = "International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings",
}