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 |
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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 | |
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 |
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Conference
Conference | 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 |
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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 |
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Deutsche Forschungsgemeinschaft | <Ke890/10-1 |
Keywords
- Gaussian Process Regression
- Manifold Learning
- SRP-PHAT