Urban sound classifcation 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 classifcation 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 (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 classifcation using neural networks has recently produced very accurate
results. A large number of diferent applications use this type of sound classifers
such as controlling and monitoring the type of activity ... [+]
Sound classifcation using neural networks has recently produced very accurate
results. A large number of diferent applications use this type of sound classifers
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 fexible and energy-efcient systems. Although
software-based platforms exist for implementing general-purpose neural networks,
they are not optimized for sound classifcation, 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-efcient circuits. The results show that our developments are accelerated by a factor of 35 compared to a software-based implementation on a Raspberry
Pi. [-]
Datos relacionados
No additional data or materials available.Entidad financiadora
CRUE-CSIC agreement with Springer Nature | NextGenerationEU/PRTR | Ministerio de Ciencia, Innovación y Universidades | Generalitat Valenciana
Código del proyecto o subvención
MCIN/AEI/10.13039/501100011033 | PID2020-113656RB | PID2021-124280OB-C21 | PID2022-137048OA-C43 | TED2021-131401B-C21 (DIPSY-AI) | TED2021-131401A-C22 (DIPSYTECH) | CIDEXG/2022/013
Título del proyecto o subvención
ERDF A way of making Europe
Derechos de acceso
© The Author(s) 2024
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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