Real-Time Modeling of Audio Distortion Circuits with Deep Learning

Eero-Pekka Damskägg, Lauri Juvela, Vesa Välimäki

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

26 Citations (Scopus)
706 Downloads (Pure)


This paper studies deep neural networks for modeling of audio distortion circuits. The selected approach is black-box modeling, which estimates model parameters based on the measured input and output signals of the device. Three common audio distortion pedals having a different circuit configuration and their own distinctive sonic character have been chosen for this study: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. A feedforward deep neural network, which is a variant of the WaveNet architecture, is proposed for modeling these devices. The size of the receptive field of the neural network is selected based on the measured impulse-response length of the circuits. A real-time implementation of the deep neural network is presented, and it is shown that the trained models can be run in real time on a modern desktop computer. Furthermore, it is shown that three minutes of audio is a sufficient amount of data for training the models. The deep neural network studied in this work is useful for real-time virtual analog modeling of nonlinear audio circuits.
Original languageEnglish
Title of host publicationProceedings of the 16th Sound & Music Computing Conference SMC 2019
ISBN (Electronic)978-84-09-08518-7
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventSound and Music Computing Conference - E.T.S.I. Telecomunicación, Malaga, Spain
Duration: 28 May 201931 May 2019
Conference number: 16

Publication series

NameProceedings of the Sound and Music Computing Conferences
ISSN (Electronic)2518-3672


ConferenceSound and Music Computing Conference
Abbreviated titleSMC
Internet address


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