扩散噪声环境下的多通道盲语音分离方法

Translated title of the contribution: Multi-channel blind speech separation method for diffuse noise environments

Shengdong Liu, Feiran Yang*, Mou Wang, Zhuo Li, Jun Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Independent vector extraction is an advanced method for blind source separation (BSS) in diffuse noise environments, suitable for overdetermined systems and characterized by high computational efficiency. However, it is limited by the assumption of stationary Gaussian noise, which makes it ineffective at eliminating diffuse noise that is aligned with the target source. To tackle this issue, a multi-channel blind source separation method is proposed for diffuse noise environments. This method assumes that the energy distribution of diffuse noise is uniform in all directions and exhibits time-varying characteristics. A low-rank source model and a rank-1 spatial model are utilized to construct a probabilistic model for the noisy mixtures. Using this probabilistic model, the update formula for the separation matrix is derived based on the maximum likelihood criterion, and the power spectral densities of the speech and noise components are estimated. Subsequently, Wiener filtering is employed to suppress the noise components that are aligned with the target source direction. Ultimately, experimental results demonstrate that the proposed method significantly outperforms existing BSS algorithms in terms of source separation performance and noise suppression capability, thus validating its effectiveness in complex acoustic environments.

Translated title of the contributionMulti-channel blind speech separation method for diffuse noise environments
Original languageChinese (Simplified)
Pages (from-to)1304-1314
Number of pages11
JournalShengxue Xuebao/Acta Acustica
Volume49
Issue number6
DOIs
Publication statusPublished - Nov 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Science Press. All rights reserved.

ASJC Scopus Subject Areas

  • Acoustics and Ultrasonics

Keywords

  • Blind source separation
  • Diffuse noise
  • Independent low-rank matrix analysis
  • Non-negative matrix factorization

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