Speech Dereverberation with Deconvolution Regularized by Denoising

Haonan Hu, Ziye Yang, Jie Chen, Lijun Zhang

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Deconvolution-based speech dereverberation continues to present challenges due to the difficulties in accurately acquiring Room Impulse Responses (RIRs) and the inherently ill-conditioned nature of deconvolution. Despite advancements in RIR measurement and estimation, substantial room for improvement remains in addressing the latter challenge. This paper proposes a novel prior-driven dereverberation framework utilizing Regularization by Denoising (RED) to incorporate data priors into the deconvolution process, thereby addressing this persistent challenge. Specifically, we formulate the dereverberation process via an optimization problem with the additional regularizer and the Half Quadratic Splitting (HQS) strategy is then utilized to solve the optimization problem. Experimental validation conducted on both the RIR simulation platform pyroomacoustics and the realistic acoustics platform SoundSpaces demonstrates the efficacy of our framework, even in the presence of environmental noise and RIR errors.

源语言英语
主期刊名APSIPA ASC 2024 - Asia Pacific Signal and Information Processing Association Annual Summit and Conference 2024
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9798350367331
DOI
出版状态已出版 - 2024
活动2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2024 - Macau, 中国
期限: 3 12月 20246 12月 2024

出版系列

姓名APSIPA ASC 2024 - Asia Pacific Signal and Information Processing Association Annual Summit and Conference 2024

会议

会议2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2024
国家/地区中国
Macau
时期3/12/246/12/24

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