Detail publikačního výsledku

Fast Temporal Convolutions for Real-Time Audio Signal Processing

MIKLÁNEK, Š.; SCHIMMEL, J.

Originální název

Fast Temporal Convolutions for Real-Time Audio Signal Processing

Anglický název

Fast Temporal Convolutions for Real-Time Audio Signal Processing

Druh

Stať ve sborníku v databázi WoS či Scopus

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

Anglický 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

Klíčová slova v angličtině

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

Autoři

MIKLÁNEK, Š.; SCHIMMEL, J.

Rok RIV

2023

Vydáno

02.09.2022

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

Plný text v Digitální knihovně

http://hdl.handle.net/

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"
}