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

Hideaki Kagami, Hirokazu Kameoka, Masahiro Yukawa

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

    1 Citation (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages561-565
    Number of pages5
    ISBN (Electronic)9781509041176
    DOIs
    Publication statusPublished - 2017 Jun 16
    Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
    Duration: 2017 Mar 52017 Mar 9

    Other

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

    Keywords

    • Audio source separation
    • non-negative matrix factorization(NMF)
    • projected gradient method

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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  • Cite this

    Kagami, H., Kameoka, H., & Yukawa, M. (2017). A majorization-minimization algorithm with projected gradient updates for time-domain spectrogram factorization. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 561-565). [7952218] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952218