Efficient relative transfer function estimation framework in the spherical harmonics domain

Yoav Biderman, Boaz Rafaely, Sharon Gannot, Simon Doclo

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

2 Scopus citations

Abstract

In acoustic conditions with reverberation and coherent sources, various spatial filtering techniques, such as the linearly constrained minimum variance (LCMV) beamformer, require accurate estimates of the relative transfer functions (RTFs) between the sensors with respect to the desired speech source. However, the time-domain support of these RTFs may affect the estimation accuracy in several ways. First, short RTFs justify the multiplicative transfer function (MTF) assumption when the length of the signal time frames is limited. Second, they require fewer parameters to be estimated, hence reducing the effect of noise and model errors. In this paper, a spherical microphone array based framework for RTF estimation is presented, where the signals are transformed to the spherical harmonics (SH)-domain. The RTF time-domain supports are studied under different acoustic conditions, showing that SH-domain RTFs are shorter compared to conventional space-domain RTFs.

Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1658-1662
Number of pages5
ISBN (Electronic)9780992862657
DOIs
StatePublished - 28 Nov 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: 28 Aug 20162 Sep 2016

Publication series

NameEuropean Signal Processing Conference
Volume2016-November
ISSN (Print)2219-5491

Conference

Conference24th European Signal Processing Conference, EUSIPCO 2016
Country/TerritoryHungary
CityBudapest
Period28/08/162/09/16

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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