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Publication detail
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
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
Pages to
6
Pages count
URL
http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/analog-clipping-circuit-simulation-with-recurrent-neural-networks/
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/" }