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GEOMETRICALLY CONSTRAINED SOURCE EXTRACTION AND DEREVERBERATION BASED ON JOINT OPTIMIZATION

  • Center of Intelligent Acoustics and Immersive Communications
  • Northwestern Polytechnical University Xian
  • Multimedia Communications and Signal Processing
  • Friedrich-Alexander University Erlangen-Nürnberg

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

6 引用 (Scopus)

摘要

Source extraction, which aims at extracting the target source signals from the observed reverberant mixtures, plays an important role in voice communication and human-machine interfaces. Among the numerous source extraction methods that have been developed, the geometrically constrained (GC) one, which incorporates the direction-of-arrival (DOA) information of the target signals, has demonstrated great potential. However, this method generally suffers from significant performance degradation in strong reverberant environments since it is challenging to obtain in such environments accurate DOA estimates that are needed by the algorithm. To address this problem, we present in this work an iterative algorithm, which integrates the source-wise weighted prediction error (WPE)based dereverberation principle with the geometrically constrained source extraction method. We show that this algorithm is able to improve the DOA estimation accuracy as well as the source extraction performance.

源语言英语
主期刊名31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
出版商European Signal Processing Conference, EUSIPCO
页41-44
页数4
ISBN(电子版)9789464593600
DOI
出版状态已出版 - 2023
活动31st European Signal Processing Conference, EUSIPCO 2023 - Helsinki, 芬兰
期限: 4 9月 2023 → 8 9月 2023

丛书

姓名European Signal Processing Conference
ISSN(电子版)2076-1465

会议

会议31st European Signal Processing Conference, EUSIPCO 2023
国家/地区芬兰
市Helsinki
时期4/09/23 → 8/09/23

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