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MIKLÁNEK, Š. SCHIMMEL, J.
Original Title
Fast Temporal Convolutions for Real-Time Audio Signal Processing
Type
conference paper
Language
English
Original Abstract
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
Keywords
convolutional neural networks; deep learning; virtual analog modelling; nonlinear systems
Authors
MIKLÁNEK, Š.; SCHIMMEL, J.
Released
2. 9. 2022
Publisher
DAFx
Location
Vídeň
ISBN
978-3-200-08599-2
Book
Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22)
2413-6689
Periodical
Proceedings of the International Conference on Digital Audio Effects (DAFx)
State
Republic of Austria
Pages from
115
Pages to
121
Pages count
7
BibTex
@inproceedings{BUT178795, author="Štěpán {Miklánek} and Jiří {Schimmel}", title="Fast Temporal Convolutions for Real-Time Audio Signal Processing", booktitle="Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22)", year="2022", journal="Proceedings of the International Conference on Digital Audio Effects (DAFx)", pages="115--121", publisher="DAFx", address="Vídeň", isbn="978-3-200-08599-2", issn="2413-6689" }