MAP-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and ℓ1-norm minimization

Stefan Winter*, Walter Kellermann, Hiroshi Sawada, Shoji Makino

*この研究の対応する著者

研究成果: Article査読

79 被引用数 (Scopus)

抄録

We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures,we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori (MAP) approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the ℓ1-norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP) approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions.

本文言語English
論文番号24717
ジャーナルEurasip Journal on Advances in Signal Processing
2007
DOI
出版ステータスPublished - 2007
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • 情報システム
  • ハードウェアとアーキテクチャ
  • 電子工学および電気工学

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