Deep Ranking-Based DOA Tracking Algorithm

Renana Opochinsky, Gal Chechik, Sharon Gannot

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

8 Scopus citations

Abstract

In this study, we present a weak-supervised deep neural network-based tracking algorithm for a moving source. A triplet-loss network is trained with instantaneous spatial features to estimate the time-varying DOA. The core idea is to minimize the use of labeled samples (i.e. samples which are accurately localized, and difficult to acquire) by using instead partial knowledge drawn from an unlabeled, and much larger, dataset in which only the relative spatial ordering between the samples is known. We use a deep learning architecture that stochastically combines a triplet-ranking loss for the unlabeled samples and a spatial loss for the labelled samples and learns a nonlinear deep embedding that maps acoustic features to an azimuth angle of the source. We show that it is unnecessary to train the network with a large number of random trajectories of a moving source, and that triplets of static sources from the same locus, which can be more easily acquired, are sufficient. A simulation study demonstrates the applicability of the proposed method to dynamic problems.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1020-1024
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
ISSN (Print)2219-5491

Conference

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

Bibliographical note

Publisher Copyright:
© 2021 European Signal Processing Conference. All rights reserved.

Funding

This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 871245. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 871245.

FundersFunder number
Horizon 2020 Framework Programme871245
Horizon 2020

    Keywords

    • Acoustic source tracking
    • Deep embedding learning
    • Relative transfer function
    • Triplet-loss

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