Efficient Velvet-Noise Convolution in Multicore Processors
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Otros documentos de la autoría: BELLOCH, JOSE A.; Badia, Jose M.; León Navarro, Germán; Välimäki, Vesa
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Título
Efficient Velvet-Noise Convolution in Multicore ProcessorsFecha de publicación
2024-06-02Editor
Audio Engineering SocietyISSN
1549-4950Cita bibliográfica
Belloch, Jose Antonio; Badia, Jose M.; Leon, German; Välimäki, Vesa; 2024; Efficient Velvet-Noise Convolution in Multicore Processors [PDF]; Department of Computer Science and Engineering, Universitat Jaume I, E-12071 Castellón de la Plana, Spain; Paper ; Available from: https://aes2.org/publications/elibrary-page/?id=22637Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Velvet noise, a sparse pseudo-random signal, finds valuable applications in audio engineering, such as artificial reverberation, decorrelation filtering, and sound synthesis. These
applications rely on convolution ... [+]
Velvet noise, a sparse pseudo-random signal, finds valuable applications in audio engineering, such as artificial reverberation, decorrelation filtering, and sound synthesis. These
applications rely on convolution operations whose computational requirements depend on the
length, sparsity, and bit resolution of the velvet-noise sequence used as filter coefficients.
Given the inherent sparsity of velvet noise and its occasional restriction to a few distinct values, significant computational savings can be achieved by designing convolution algorithms
that exploit these unique properties. This paper shows that an algorithm called the transposed
double-vector filter is the most efficient way of convolving velvet noise with an audio signal.
This method optimizes access patterns to take advantage of the processor’s fast caches. The
sequential sparse algorithm is shown to be always faster than the dense one, and the speedup
is linearly dependent on sparsity. The paper also explores the potential for further speedup on
multicore platforms through parallelism and evaluate the impact of data encoding, including
16-bit and 32-bit integers and 32-bit floating-point representations. The results show that using
the fastest implementation of a long velvet-noise filter, it is possible to process more than 40
channels of audio in real time using the quad-core processor of a modern system-on-chip. [-]
Publicado en
J. Audio Eng. Soc., Vol. 72, No. 6, 2024 Jun.Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades | MCIN/AEI/10.13039/501100011033 | Gobierno Regional de Madrid
Código del proyecto o subvención
PID2022-137048OA-C43 | PID2019-106455GBC21 | PID2020-113656RB-C21 | TED2021-131401AC22 | MIMACUHSPACE-CM-UC3M
Título del proyecto o subvención
Ayuda Movilidad Programa Propio de Investigacion 2022, modalidad A: jovenes doctores, de la Universidad Carlos III de Madrid.
Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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