Detail publikace

Fast Temporal Convolutions for Real-Time Audio Signal Processing

MIKLÁNEK, Š. SCHIMMEL, J.

Originální název

Fast Temporal Convolutions for Real-Time Audio Signal Processing

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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

Klíčová slova

convolutional neural networks; deep learning; virtual analog modelling; nonlinear systems

Autoři

MIKLÁNEK, Š.; SCHIMMEL, J.

Vydáno

2. 9. 2022

Nakladatel

DAFx

Místo

Vídeň

ISBN

978-3-200-08599-2

Kniha

Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22)

ISSN

2413-6689

Periodikum

Proceedings of the International Conference on Digital Audio Effects (DAFx)

Stát

Rakouská republika

Strany od

115

Strany do

121

Strany počet

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"
}