Beamspace-domain multichannel nonnegative matrix factorization for audio source separation

Seokjin Lee, Sang Ha Park, Koeng Mo Sung

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

In this letter, we develop a multichannel blind source separation algorithm based on a beamspace transform and the multichannel nonnegative matrix factorization (NMF) method. The conventional multichannel NMF algorithm performs well with multichannel mixing data, but there is still room for enhancement in multichannel real-world recording data. In this letter, we consider a beamspace-time-frequency domain data model for multichannel NMF method, and enhance the conventional method using a beamspace transform. Our decomposition algorithm is applied to 2-channel and 4-channel unsupervised audio source separation, using a dataset from the international Signal Separation Evaluation Campaign 2010 (SiSEC 2010). Our algorithm shows a better performance than the conventional NMF method in an evaluation results.

Original languageEnglish
Article number6058587
Pages (from-to)43-46
Number of pages4
JournalIEEE Signal Processing Letters
Volume19
Issue number1
DOIs
StatePublished - 2012

Keywords

  • Acoustic signal processing blind source separation multichannel audio
  • Nonnegative matrix factorization (NMF)

Fingerprint

Dive into the research topics of 'Beamspace-domain multichannel nonnegative matrix factorization for audio source separation'. Together they form a unique fingerprint.

Cite this