Quadratic Beamforming for Magnitude Estimation

Gal Itzhak, Jacob Benesty, Israel Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we introduce an optimal quadratic Wiener beamformer for magnitude estimation of a desired signal. For simplicity, we focus on a two-microphone array and develop an iterative algorithm for magnitude estimation based on a quadratic multichannel noise reduction approach. We analyze two test cases, with uncorrelated and correlated noises. In each, we derive the appropriate versions of the Wiener beamformer, as well as their corresponding unbiased magnitude estimators. We compare the root-mean-squared errors (RMSEs) for the linear and quadratic Wiener beamformers and show that for low input signal-to-noise ratios (SNRs), the RMSE obtained with the proposed approach is either lower than or equal to the RMSE obtained with the linear Wiener beamformer, depending on the type of noise and its distribution.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
Pages251-255
Number of pages5
ISBN (Electronic)9789082797060
DOIs
StatePublished - 2021
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
Volume2021-August

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
Country/TerritoryIreland
CityDublin
Period23/08/2127/08/21

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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