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

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations

Abstract

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.

Keywords

  • convolutive transfer function
  • Independent vector analysis
  • spatially informed source extraction

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