Time-Frequency-Bin-Wise Linear Combination of Beamformers for Distortionless Signal Enhancement

Kouei Yamaoka*, Nobutaka Ono, Shoji Makino

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we address signal enhancement in underdetermined situations and propose new beamforming algorithms. Beamforming in (over) determined situations can successfully reduce noise signals without distortion of a desired signal, which is known to be a desirable property, especially for automatic speech recognition systems. Even in underdetermined situations, time-frequency (TF) masking attains outstanding performance in noise reduction, although it tends to generate artifacts. Integrating these two approaches to benefit from both their advantages, we here propose time-frequency-bin-wise switching (TFS) and time-frequency-bin-wise linear combination (TFLC) beamforming. In the proposed methods, we utilize the best combination of beamformers among multiple beamformers at each TF bin, each of which suppresses a particular combination of interferers. First, we propose a general formulation of signal enhancement employing multiple spatial filters. Then a joint optimization problem of designing the spatial filters and estimating the suitable weights to combine them is considered under a unified minimum variance criterion. Finally, we present efficient algorithms to solve the problem. In experiments, we used an objective criterion that quantifies the amount of signal distortion caused by the enhancement function and confirmed that the proposed methods effectively suppress interferers without distortion of the target signal.

Original languageEnglish
Pages (from-to)3461-3475
Number of pages15
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume29
DOIs
Publication statusPublished - 2021

Keywords

  • Beamforming
  • linear combination
  • nonlinear signal processing
  • time-frequency masking
  • underdetermined

ASJC Scopus subject areas

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

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