Projects per year
Abstract
In this paper, the problems of indoor sound source localization using a wireless acoustic sensor network are addressed and a new sparse Bayesian learning based algorithm is proposed. Using time delays for the direct paths from candidate source locations to microphone nodes, the proposed algorithm estimates the most likely source location. To reduce the amount of data that must be exchanged between microphone nodes, a Gaussian measurement matrix is multiplied on to each channel and the proposed method operates directly on the compressed data. This is achieved by exploiting sparsity in both the frequency and space domains. The performance is analysed in numerical simulations, where the performance as a function of the reverberation times in investigated, and the results show that the proposed algorithm is robust to reverberation.
Original language | English |
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Title of host publication | 2019 27th European Signal Processing Conference (EUSIPCO) |
Publisher | IEEE |
Publication date | 2019 |
Article number | 8903069 |
ISBN (Print) | 978-90-827970-2-2 (USB) |
ISBN (Electronic) | 978-9-0827-9703-9 |
DOIs | |
Publication status | Published - 2019 |
Event | 27th European Signal Processing Conference, EUSIPCO 2019 - Coruña, Spain Duration: 2 Sept 2019 → 6 Sept 2019 |
Conference
Conference | 27th European Signal Processing Conference, EUSIPCO 2019 |
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Country/Territory | Spain |
City | Coruña |
Period | 02/09/2019 → 06/09/2019 |
Series | Proceedings of the European Signal Processing Conference |
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ISSN | 2076-1465 |
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Dive into the research topics of 'Indoor Sound Source Localization based on Sparse Bayesian Learning and Compressed Data'. Together they form a unique fingerprint.Projects
- 1 Finished
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Sound PrOcessing for Robots and Drones in the fourth industrial revolution
01/01/2018 → 31/12/2020
Project: Research