Analog Clipping Circuit Simulation with Recurrent Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140670" target="_blank" >RIV/00216305:26220/21:PU140670 - isvavai.cz</a>
Result on the web
<a href="http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/analog-clipping-circuit-simulation-with-recurrent-neural-networks/" target="_blank" >http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/analog-clipping-circuit-simulation-with-recurrent-neural-networks/</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Analog Clipping Circuit Simulation with Recurrent Neural Networks
Original language description
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.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Elektrorevue - Internetový časopis (http://www.elektrorevue.cz)
ISSN
1213-1539
e-ISSN
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Volume of the periodical
23
Issue of the periodical within the volume
1
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
6
Pages from-to
1-6
UT code for WoS article
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EID of the result in the Scopus database
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