Multi-View Source Localization Based on Power Ratios

Bracha Laufer-Goldshtein, Ronen Talmon, Israel Cohen, Sharon Gannot

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

6 Scopus citations


Despite attracting significant research efforts, the problem of source localization in noisy and reverberant environments remains challenging. Novel learning-based methods attempt to solve the problem by modelling the acoustic environment from the observed data. Typically, appropriate feature vectors are defined, and then used for constructing a model, which maps the extracted features to the corresponding source positions. In this paper, we focus on localizing a source using a distributed network with several arrays of unidirectional microphones. We introduce new feature vectors, which utilize the special characteristic of unidirectional microphones, receiving different parts of the reverberated speech. The new features are computed locally for each array, using the power-ratios between its measured signals, and are used to construct a local model, representing the unique view point of each array. The models of the different arrays, conveying distinct and complementing structures, are merged by a Multi-View Gaussian Process (MVGP), mapping the new features to their corresponding source positions. Based on this unifying model, a Bayesian estimator is derived, exploiting the relations conveyed by the covariance terms of the MVGP. The resulting localizer is shown to be robust to noise and reverberation, utilizing a computationally efficient feature extraction.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781538646588
StatePublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

Bibliographical note

Funding Information:
1 This work is supported by the Adams Foundation of the Israel Academy of Sciences and Humanities .

Publisher Copyright:
© 2018 IEEE.


  • Gaussian process
  • Source localization
  • Supervised learning
  • Unidirectional microphones


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