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dc.contributor.author | Canseco, Sergio | |
dc.date.accessioned | 2025-02-02T10:45:07Z | |
dc.date.available | 2025-02-02T10:45:07Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | InterNoise 2022 Glasgow | es |
dc.identifier.issn | 0736-2935 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/74786 | |
dc.description.abstract | In the field of human detection using acoustic arrays, the design of beamforming and detection algorithms is of vital importance. Evidently, the acoustic echo is directly dependent on the ergonomic characteristics of the people, as well as on the clothes they are wearing. Traditional techniques use a large set of people to characterize the system and evaluate the detection and false alarm probabilities. This work proposes a different approach, where a reduced set of people is selected and a cluster of points with their ergonomic data is obtained by means of a 2D LIDAR. From this data and using a classical reflection model, the signals that would be received in an acoustic array are calculated and, using beamforming techniques, the 3D acoustic image is obtained. The work compares these synthesized acoustic images with real acoustic ones. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Proceedings of the Institute of Acoustic | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | * |
dc.title | Acoustic echo modeling of people in acoustic arrays using LIDAR | es |
dc.type | info:eu-repo/semantics/article | es |
dc.identifier.doi | 10.3397/IN_2022_0533 | es |
dc.identifier.publicationfirstpage | 3777 | es |
dc.identifier.publicationissue | 4 | es |
dc.identifier.publicationlastpage | 3784 | es |
dc.identifier.publicationtitle | INTER-NOISE and NOISE-CON Congress and Conference Proceedings | es |
dc.identifier.publicationvolume | 265 | es |
dc.peerreviewed | SI | es |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Unported | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
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