Speech Enhancement Based on Bayesian Low-Rank and Sparse Decomposition of Multichannel Magnitude Spectrograms

Yoshiaki Bando, Katsutoshi Itoyama, Masashi Konyo, Satoshi Tadokoro, Kazuhiro Nakadai, Kazuyoshi Yoshii, Tatsuya Kawahara, Hiroshi G. Okuno

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


This paper presents a blind multichannel speech enhancement method that can deal with the time-varying layout of microphones and sound sources. Since nonnegative tensor factorization (NTF) separates a multichannelmagnitude (or power) spectrogram into source spectrograms without phase information, it is robust against the time-varying mixing system. This method, however, requires prior information such as the spectral bases (templates) of each source spectrogram in advance. To solve this problem, we develop a Bayesian model called robust NTF (Bayesian RNTF) that decomposes a multichannel magnitude spectrogram into target speech and noise spectrograms based on their sparseness and low rankness. Bayesian RNTF is applied to the challenging task of speech enhancement for a microphone array distributed on a hose-shaped rescue robot. When the robot searches for victims under collapsed buildings, the layout of themicrophones changes over time and some of them often fail to capture target speech. Our method robustly works under such situations, thanks to its characteristic of time-varying mixing system. Experiments using a 3-m hose-shaped rescue robot with eight microphones show that the proposed method outperforms conventional blind methods in enhancement performance by the signal-to-noise ratio of 1.03 dB.

Original languageEnglish
Pages (from-to)215-230
Number of pages16
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Issue number2
Publication statusPublished - 2018 Feb


  • Bayesian signal processing
  • low-rank and sparse decomposition
  • Multichannel speech enhancement


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