Publication detail

Analog Clipping Circuit Simulation with Recurrent Neural Networks

MIKLÁNEK, Š.

Original Title

Analog Clipping Circuit Simulation with Recurrent Neural Networks

Type

journal article - other

Language

English

Original Abstract

This article focuses on the practical use of recurrent neural networks for the simulation of analog audio circuits. Two virtual analog circuits were modeled using the Long Short-Term Memory neural networks. The neural network models presented in earlier literature were compared against newly proposed architectures, which used additional fully connected input layers. The signals processed by the neural network models of different complexity were compared to the ground truth data generated using the LTSpice software. It was found that the modifications done to the previously proposed neural network architectures can reduce the resulting prediction loss without significant increase in complexity.

Keywords

recurrent neural networks, signal modeling, audio clipping circuits

Authors

MIKLÁNEK, Š.

Released

29. 3. 2021

Publisher

Elektrorevue

Location

Brno

ISBN

1213-1539

Periodical

Elektrorevue - Internetový časopis (http://www.elektrorevue.cz)

Year of study

23

Number

1

State

Czech Republic

Pages from

1

Pages to

6

Pages count

6

URL

BibTex

@article{BUT171249,
  author="Štěpán {Miklánek}",
  title="Analog Clipping Circuit Simulation with Recurrent Neural Networks",
  journal="Elektrorevue - Internetový časopis (http://www.elektrorevue.cz)",
  year="2021",
  volume="23",
  number="1",
  pages="1--6",
  issn="1213-1539",
  url="http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/analog-clipping-circuit-simulation-with-recurrent-neural-networks/"
}