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Spatially Informed Independent vector analysis for Source Extraction based on the convolutive Transfer Function Model

  • Xianrui Wang
  • , Andreas Brendel
  • , Gongping Huang
  • , Yichen Yang
  • , Walter Kellermann
  • , Jingdong Chen
  • Northwestern Polytechnical University Xian
  • 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

11 引用 (Scopus)

摘要

Spatial information can help improve source separation performance. Numerous spatially informed source extraction methods based on the independent vector analysis (IVA) have been developed, which can achieve reasonably good performance in non- or weakly reverberant environments. However, the performance of those methods degrades quickly as the reverberation increases. The underlying reason is that those methods are derived based on the multiplicative transfer function model with a rank-1 assumption, which does not hold true if reverberation is strong. To circumvent this issue, this paper proposes to use the convolutive transfer function (CTF) model to improve the source extraction performance and develop a spatially informed IVA algorithm. Simulations demonstrate the efficacy of the developed method even in highly reverberant environments.

源语言英语
主期刊名ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9781728163277
DOI
出版状态已出版 - 2023
活动48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, 希腊
期限: 4 6月 202310 6月 2023

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2023-June
ISSN(印刷版)1520-6149

会议

会议48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
国家/地区希腊
Rhodes Island
时期4/06/2310/06/23

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