Determined audio source separation with multichannel star generative adversarial network

Li Li, Hirokazu Kameoka, Shoji Makino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper proposes a multichannel source separation approach, which uses a star generative adversarial network (StarGAN) to model power spectrograms of sources. Various studies have shown the significant contributions of a precise source model to the performance improvement in audio source separation, which indicates the importance of developing a better source model. In this paper, we explore the potential of StarGAN for modeling source spectrograms and investigate the effectiveness of the StarGAN source model in determined multichannel source separation by incorporating it into a frequency-domain independent component analysis (ICA) framework. The experimental results reveal that the proposed StarGAN-based method outperformed conventional methods that use non-negative matrix factorization (NMF) or a variational autoencoder (VAE) for source spectrogram modeling.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728166629
DOIs
Publication statusPublished - 2020 Sep
Externally publishedYes
Event30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 - Virtual, Espoo, Finland
Duration: 2020 Sep 212020 Sep 24

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2020-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
Country/TerritoryFinland
CityVirtual, Espoo
Period20/9/2120/9/24

Keywords

  • Deep generative model
  • Determined source separation
  • Multichannel audio signal processing
  • Spectrogram modeling
  • Star generative adversarial network (StarGAN)

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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