Learning-Based Acoustic Source Localization Using Directional Spectra

Andreas Brendel; Bracha Laufer-Goldshtein; Sharon Gannot; Walter Kellermann

doi:10.1109/camsap45676.2019.9022522

Learning-Based Acoustic Source Localization Using Directional Spectra

Andreas Brendel, Bracha Laufer-Goldshtein, Sharon Gannot, Walter Kellermann

Friedrich-Alexander University Erlangen-Nürnberg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

This paper proposes to use directional spectra as new features for manifold-learning-based acoustic source localization. We claim that directional spectra not only contain directional information, but are rather discriminative for different positions in a reverberant enclosure. We use these proposed features to build a manifold-learning-based localization algorithm which is applied to single-array localization as well as to Acoustic Sensor Network (ASN) localization. The performance of the proposed algorithm is benchmarked by comprehensive experiments carried out in a simulated environment, with comparison to a blind approach based on triangulation, as well as by Gaussian Process Regression (GPR)-based localization.

Original language	English
Title of host publication	2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	276-280
Number of pages	5
ISBN (Electronic)	9781728155494
DOIs	https://doi.org/10.1109/camsap45676.2019.9022522
State	Published - Dec 2019
Event	8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Le Gosier, Guadeloupe Duration: 15 Dec 2019 → 18 Dec 2019

Publication series

Name	2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings

Conference

Conference	8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Country/Territory	Guadeloupe
City	Le Gosier
Period	15/12/19 → 18/12/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Funding

This work was supported by DFG under contract no <Ke890/10-1> within the Research Unit FOR2457 “AcousticSensor Networks”

Funders	Funder number
Deutsche Forschungsgemeinschaft	<Ke890/10-1

Keywords

Gaussian Process Regression
Manifold Learning
SRP-PHAT

Access to Document

10.1109/camsap45676.2019.9022522

Cite this

Brendel, A., Laufer-Goldshtein, B., Gannot, S., & Kellermann, W. (2019). Learning-Based Acoustic Source Localization Using Directional Spectra. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings (pp. 276-280). Article 9022522 (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/camsap45676.2019.9022522

Brendel, Andreas ; Laufer-Goldshtein, Bracha ; Gannot, Sharon et al. / Learning-Based Acoustic Source Localization Using Directional Spectra. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 276-280 (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings).

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title = "Learning-Based Acoustic Source Localization Using Directional Spectra",

abstract = "This paper proposes to use directional spectra as new features for manifold-learning-based acoustic source localization. We claim that directional spectra not only contain directional information, but are rather discriminative for different positions in a reverberant enclosure. We use these proposed features to build a manifold-learning-based localization algorithm which is applied to single-array localization as well as to Acoustic Sensor Network (ASN) localization. The performance of the proposed algorithm is benchmarked by comprehensive experiments carried out in a simulated environment, with comparison to a blind approach based on triangulation, as well as by Gaussian Process Regression (GPR)-based localization.",

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year = "2019",

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Brendel, A, Laufer-Goldshtein, B, Gannot, S & Kellermann, W 2019, Learning-Based Acoustic Source Localization Using Directional Spectra. in 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings., 9022522, 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 276-280, 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019, Le Gosier, Guadeloupe, 15/12/19. https://doi.org/10.1109/camsap45676.2019.9022522

Learning-Based Acoustic Source Localization Using Directional Spectra. / Brendel, Andreas; Laufer-Goldshtein, Bracha; Gannot, Sharon et al.
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 276-280 9022522 (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper proposes to use directional spectra as new features for manifold-learning-based acoustic source localization. We claim that directional spectra not only contain directional information, but are rather discriminative for different positions in a reverberant enclosure. We use these proposed features to build a manifold-learning-based localization algorithm which is applied to single-array localization as well as to Acoustic Sensor Network (ASN) localization. The performance of the proposed algorithm is benchmarked by comprehensive experiments carried out in a simulated environment, with comparison to a blind approach based on triangulation, as well as by Gaussian Process Regression (GPR)-based localization.

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Brendel A, Laufer-Goldshtein B, Gannot S, Kellermann W. Learning-Based Acoustic Source Localization Using Directional Spectra. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 276-280. 9022522. (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings). doi: 10.1109/camsap45676.2019.9022522

Learning-Based Acoustic Source Localization Using Directional Spectra

Abstract

Publication series

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Bibliographical note

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Keywords

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