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Urban sound classification using neural networks on embedded FPGAs
dc.contributor.author | BELLOCH, JOSE A. | |
dc.contributor.author | Coronado, Raul | |
dc.contributor.author | Valls, Oscar | |
dc.contributor.author | Amor, Rocío del | |
dc.contributor.author | Leon, German | |
dc.contributor.author | Naranjo, Valery | |
dc.contributor.author | Dolz, Manuel F. | |
dc.contributor.author | Amor-Martin, Adrian | |
dc.contributor.author | Piñero, Gema | |
dc.date.accessioned | 2024-06-11T10:47:47Z | |
dc.date.available | 2024-06-11T10:47:47Z | |
dc.date.issued | 2024-03-01 | |
dc.identifier.citation | Belloch, J.A., Coronado, R., Valls, O. et al. Urban sound classification using neural networks on embedded FPGAs. J Supercomput 80, 13176–13186 (2024). https://doi.org/10.1007/s11227-024-05947-8 | ca_CA |
dc.identifier.issn | 0920-8542 | |
dc.identifier.issn | 1573-0484 | |
dc.identifier.uri | http://hdl.handle.net/10234/207791 | |
dc.description.abstract | Sound classification using neural networks has recently produced very accurate results. A large number of different applications use this type of sound classifiers such as controlling and monitoring the type of activity in a city or identifying different types of animals in natural environments. While traditional acoustic processing applications have been developed on high-performance computing platforms equipped with expensive multi-channel audio interfaces, the Internet of Things (IoT) paradigm requires the use of more flexible and energy-efficient systems. Although software-based platforms exist for implementing general-purpose neural networks, they are not optimized for sound classification, wasting energy and computational resources. In this work, we have used FPGAs to develop an ad hoc system where only the hardware needed for our application is synthesized, resulting in faster and more energy-efficient circuits. The results show that our developments are accelerated by a factor of 35 compared to a software-based implementation on a Raspberry Pi. | ca_CA |
dc.description.sponsorShip | Funding for open access charge: CRUE-CSIC agreement with Springer Nature | |
dc.format.extent | 11 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer | ca_CA |
dc.relation.isPartOf | The Journal of Supercomputing (2024) 80:13176–13186 | ca_CA |
dc.relation.uri | No additional data or materials available. | ca_CA |
dc.rights | © The Author(s) 2024 | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | FPGA | ca_CA |
dc.subject | sound classifcation | ca_CA |
dc.subject | hardware acceleration | ca_CA |
dc.subject | convolutional neural networks | ca_CA |
dc.subject | deep learning | ca_CA |
dc.title | Urban sound classification using neural networks on embedded FPGAs | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s11227-024-05947-8 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | NextGenerationEU/PRTR | ca_CA |
project.funder.name | MCIN/AEI/10.13039/501100011033 | ca_CA |
project.funder.name | PROGRAMA MIMACUHSPACE-CM-UC3M | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
oaire.awardNumber | PID2020-113656RB | ca_CA |
oaire.awardNumber | PID2021-124280OB-C21 | ca_CA |
oaire.awardNumber | TED2021-131401B-C21 (DIPSY-AI) | ca_CA |
oaire.awardNumber | TED2021-131401A-C22 (DIPSYTECH) | ca_CA |
oaire.awardNumber | PID2022-137048OA-C43 | ca_CA |
oaire.awardNumber | 2023-CIPROM/2022/20 | ca_CA |
oaire.awardNumber | CIDEXG/2022/013 | ca_CA |
dc.subject.ods | 7. Energia asequible y no contaminante | ca_CA |
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