Fast Temporal Convolutions for Real-Time Audio Signal Processing

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU145255" target="_blank" >RIV/00216305:26220/22:PU145255 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Fast Temporal Convolutions for Real-Time Audio Signal Processing
Original language description
This paper introduces the possibilities of optimizing neural network convolutional layers for modeling nonlinear audio systems and effects. Enhanced methods for real-time dilated convolutions are presented to achieve faster signal processing times than in previous work. Due to the improved implementation of convolutional layers, a significant decrease in computational requirements was observed and validated on different configurations of single layers with dilated convolutions and WaveNet-style feedforward neural network models. In most cases, equivalent signal processing times were achieved to those using recurrent neural networks with Long Short-Term Memory units and Gated Recurrent Units, which are considered state-of-the-art in the field of black-box virtual analog modeling
Czech name
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Czech description
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Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22)
ISBN
978-3-200-08599-2
ISSN
2413-6689
e-ISSN
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Number of pages
7
Pages from-to
115-121
Publisher name
DAFx
Place of publication
Vídeň
Event location
Vídeň
Event date
Sep 6, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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