Sparse Distortionless Beamformer Based on Nonconvex Optimization

Taiga Kawamura, Kohei Yatabe, Ryoichi Miyazaki

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

Abstract

Minimum power distortionless response (MPDR) beamformer is a popular beamformer that minimizes its output power under the distortionless constraint. To improve the performance of the MPDR beamformer, a sparse distortionless beamformer has been proposed. It minimizes the ℓ1 norm of the output signal under the same constraint so that the output has sparse time-frequency representation. While the ℓ1-norm-based formulation is theoretically advantageous owing to its convexity, its practical performance might not be excellent because of the bias of the ℓ1 norm. To reduce the bias and improve the performance, we propose a sparse distortionless beamformer based on a nonconvex sparsity-inducing objective function. The proposed beamformer is performed via a heuristic application of a primal-dual splitting algorithm. The experiments showed that the proposed beamformer can achieve higher performance and is more robust against mismatch of the target direction.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages281-285
Number of pages5
ISBN (Electronic)9789082797060
DOIs
Publication statusPublished - 2021
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 2021 Aug 232021 Aug 27

Publication series

NameEuropean Signal Processing Conference
Volume2021-August
ISSN (Print)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
Country/TerritoryIreland
CityDublin
Period21/8/2321/8/27

Keywords

  • Microphone array
  • Proximal algorithm
  • Sparse time-frequency representation
  • Speech enhancement

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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