Sparsity and low-rank amplitude based blind Source Separation

Fangchen Feng; Matthieu Kowalski

ICASSP 2017accepted0 citations

Sparsity and low-rank amplitude based blind Source Separation

Abstract

This paper presents a new method for blind source separation problem in reverberant environments with more sources than microphones. Based on the sparsity property in the time-frequency domain and the low-rank assumption of the spectrogram of the source, the STRAUSS (SparsiTy and low-Rank AmplitUde based Source Separation) method is developed. Numerical evaluations show that the proposed method outperforms the existing multichannel NMF approaches, while it is exclusively based on amplitude information.

BibTeX

@inproceedings{icassp2017_sparsityandlowra,
  title = {Sparsity and low-rank amplitude based blind Source Separation},
  author = {Fangchen Feng and Matthieu Kowalski},
  booktitle = {ICASSP 2017},
  year = {2017}
}