A majorization-minimization algorithm with projected gradient updates for time-domain spectrogram factorization

Hideaki Kagami, Hirokazu Kameoka, Masahiro Yukawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We previously introduced a framework called time-domain spectrogram factorization (TSF), which realizes nonnegative matrix factorization (NMF)-like source separation in the time domain. This framework is particularly noteworthy in that, while maintaining the ability of NMF to obtain a parts-based representation of magnitude spectra, it allows us to (i) circumvent the commonly made assumption with the NMF approach that the magnitude spectra of source components are additive and (ii) take account of the interdependence of the phase/amplitude components at different time-frequency points. In particular, the second factor has been overlooked despite its potential importance. Our previous study revealed that the conventional TSF algorithm was relatively slow due to large matrix inversions, and the early stopping of the algorithm often resulted in poor separation accuracy. To overcome this problem, this paper presents an iterative TSF solver using projected gradient updates. Simulation results show that the proposed TSF approach yields higher source separation performance than NMF and the other variants including the original TSF.

本文言語English
ホスト出版物のタイトル2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ561-565
ページ数5
ISBN(電子版)9781509041176
DOI
出版ステータスPublished - 2017 6 16
イベント2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
継続期間: 2017 3 52017 3 9

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period17/3/517/3/9

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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