Detail publikace

Analog Clipping Circuit Simulation with Recurrent Neural Networks

MIKLÁNEK, Š.

Originální název

Analog Clipping Circuit Simulation with Recurrent Neural Networks

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

recurrent neural networks, signal modeling, audio clipping circuits

Autoři

MIKLÁNEK, Š.

Vydáno

29. 3. 2021

Nakladatel

Elektrorevue

Místo

Brno

ISSN

1213-1539

Periodikum

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

Ročník

23

Číslo

1

Stát

Česká republika

Strany od

1

Strany do

6

Strany počet

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/"
}