Determined BSS Based on Time-Frequency Masking and Its Application to Harmonic Vector Analysis

Kohei Yatabe*, Daichi Kitamura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


This paper proposes harmonic vector analysis (HVA) based on a general algorithmic framework of audio blind source separation (BSS) that is also presented in this paper. BSS for a convolutive audio mixture is usually performed by multichannel linear filtering when the numbers of microphones and sources are equal (determined situation). This paper addresses such determined BSS based on batch processing. To estimate the demixing filters, effective modeling of the source signals is important. One successful example is independent vector analysis (IVA) that models the signals via co-occurrence among the frequency components in each source. To give more freedom to the source modeling, a general framework of determined BSS is presented in this paper. It is based on the plug-and-play scheme using a primal-dual splitting algorithm and enables us to model the source signals implicitly through a time-frequency mask. By using the proposed framework, determined BSS algorithms can be developed by designing masks that enhance the source signals. As an example of its application, we propose HVA by defining a time-frequency mask that enhances the harmonic structure of audio signals via sparsity of cepstrum. The experiments showed that HVA outperforms IVA and independent low-rank matrix analysis (ILRMA) for both speech and music signals. A MATLAB code is provided along with the paper for a reference.

Original languageEnglish
Article number9406343
Pages (from-to)1609-1625
Number of pages17
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Publication statusPublished - 2021


  • Blind source separation (BSS)
  • Wiener-like mask
  • cepstrum analysis
  • independent component analysis (ICA)
  • plug-and-play scheme
  • proximal splitting algorithm

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering


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