Semi-supervised source localization with deep generative modeling

Michael J. Bianco, Sharon Gannot, Peter Gerstoft

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

18 Scopus citations

Abstract

We propose a semi-supervised localization approach based on deep generative modeling with variational autoencoders (VAE). Localization in reverberant environments remains a challenge, which machine learning (ML) has shown promise in addressing. Even with large data volumes, the number of labels available for supervised learning in reverberant environments is usually small. We address this issue by perform semi-supervised learning (SSL) with convolutional VAEs. The VAE is trained to generate the phase of relative transfer functions (RTFs), in parallel with a DOA classifier, on both labeled and unlabeled RTF samples. The VAE-SSL approach is compared with SRP-PHAT and fully-supervised CNNs. We find that VAE-SLL can outperform both SRP-PHAT and CNN in label-limited scenarios.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728166629
DOIs
StatePublished - Sep 2020
Event30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 - Virtual, Espoo, Finland
Duration: 21 Sep 202024 Sep 2020

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2020-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
Country/TerritoryFinland
CityVirtual, Espoo
Period21/09/2024/09/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Deep learning
  • Generative modeling
  • Semi-supervised learning
  • Source localization

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