Urban sound classification using neural networks on embedded FPGAs
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Otros documentos de la autoría: BELLOCH, JOSE A.; Coronado, Raul; Valls, Oscar; Amor, Rocío del; Leon, German; Naranjo, Valery; Dolz, Manuel F.; Amor-Martin, Adrian; Piñero, Gema
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Título
Urban sound classification using neural networks on embedded FPGAsAutoría
Fecha de publicación
2024-03-01Editor
SpringerISSN
0920-8542; 1573-0484Cita bibliográfica
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/s11227-024-05947-8Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
The Journal of Supercomputing (2024) 80:13176–13186Datos relacionados
No additional data or materials available.Entidad financiadora
NextGenerationEU/PRTR | MCIN/AEI/10.13039/501100011033 | PROGRAMA MIMACUHSPACE-CM-UC3M | Generalitat Valenciana
Código del proyecto o subvención
PID2020-113656RB | PID2021-124280OB-C21 | TED2021-131401B-C21 (DIPSY-AI) | TED2021-131401A-C22 (DIPSYTECH) | PID2022-137048OA-C43 | 2023-CIPROM/2022/20 | CIDEXG/2022/013
Derechos de acceso
© The Author(s) 2024
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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