Towards a generalization of relative transfer functions to more than one source

Antoine Deleforge, Sharon Gannot, Walter Kellermann

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

Abstract

We propose a natural way to generalize relative transfer functions (RTFs) to more than one source. We first prove that such a generalization is not possible using a single multichannel spectro-temporal observation, regardless of the number of microphones. We then introduce a new transform for multichannel multi-frame spectrograms, i.e., containing several channels and time frames in each time-frequency bin. This transform allows a natural generalization which satisfies the three key properties of RTFs, namely, they can be directly estimated from observed signals, they capture spatial properties of the sources and they do not depend on emitted signals. Through simulated experiments, we show how this new method can localize multiple simultaneously active sound sources using short spectro-temporal windows, without relying on source separation.

Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages419-423
Number of pages5
ISBN (Electronic)9780992862633
DOIs
StatePublished - 22 Dec 2015
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: 31 Aug 20154 Sep 2015

Publication series

Name2015 23rd European Signal Processing Conference, EUSIPCO 2015

Conference

Conference23rd European Signal Processing Conference, EUSIPCO 2015
Country/TerritoryFrance
CityNice
Period31/08/154/09/15

Keywords

  • Grassmannian manifolds
  • Multiple sound sources localization
  • Plucker Embedding
  • Relative Transfer Function

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

Fingerprint

Dive into the research topics of 'Towards a generalization of relative transfer functions to more than one source'. Together they form a unique fingerprint.

Cite this