Real-Time Modeling of Audio Distortion Circuits with Deep Learning

Eero-Pekka Damskägg, Lauri Juvela, Vesa Välimäki

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

188 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 16th Sound & Music Computing Conference SMC 2019
Sivut332-339
ISBN (elektroninen)978-84-09-08518-7
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaSound and Music Computing Conference - E.T.S.I. Telecomunicación, Malaga, Espanja
Kesto: 28 toukokuuta 201931 toukokuuta 2019
Konferenssinumero: 16
http://smc2019.uma.es/

Julkaisusarja

NimiProceedings of the Sound and Music Computing Conferences
ISSN (elektroninen)2518-3672

Conference

ConferenceSound and Music Computing Conference
LyhennettäSMC
MaaEspanja
KaupunkiMalaga
Ajanjakso28/05/201931/05/2019
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