Deep Learning for Tube Amplifier Emulation

Eero-Pekka Damskägg; Lauri Juvela; Etienne Thuillier; Vesa Välimäki

doi:10.1109/ICASSP.2019.8682805

Deep Learning for Tube Amplifier Emulation

Eero-Pekka Damskägg, Lauri Juvela, Etienne Thuillier, Vesa Välimäki

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

45 Sitaatiot (Scopus)

Abstrakti

Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities. Faithfully modeling such significant aspect of the original sound in virtual analog software can prove challenging. The current work proposes a generic data-driven approach to virtual analog modeling and applies it to the Fender Bassman 56F-A vacuum-tube amplifier. Specifically, a feedforward variant of the WaveNet deep neural network is trained to carry out a regression on audio waveform samples from input to output of a SPICE model of the tube amplifier. The output signals are pre-emphasized to assist the model at learning the high-frequency content. The results of a listening test suggest that the proposed model accurately emulates the reference device. In particular, the model responds to user control changes, and faithfully restitutes the range of sonic characteristics found across the configurations of the original device.

Alkuperäiskieli	Englanti
Otsikko	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings
Kustantaja	IEEE
Sivut	471-475
Sivumäärä	5
ISBN (elektroninen)	978-1-4799-8131-1
ISBN (painettu)	978-1-4799-8132-8
DOI - pysyväislinkit	https://doi.org/10.1109/ICASSP.2019.8682805
Tila	Julkaistu - 1 toukok. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia Kesto: 12 toukok. 2019 → 17 toukok. 2019 Konferenssinumero: 44

Julkaisusarja

Nimi	Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (painettu)	1520-6149
ISSN (elektroninen)	2379-190X

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Lyhennettä	ICASSP
Maa/Alue	Iso-Britannia
Kaupunki	Brighton
Ajanjakso	12/05/2019 → 17/05/2019

Pääsy asiakirjaan

10.1109/ICASSP.2019.8682805

https://arxiv.org/abs/1811.00334

Muut tiedostot ja linkit

Link to publication in Scopus

Akustiikan laboratorio
Pulkki, V. (Manager)
Sähkötekniikan korkeakoulu
Laitteistot/tilat: Facility
Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Musiikkiteknologia: Quad Cortex - Pieni laite, oikea soundi
Vesa Välimäki
25/11/2023
1 Median myötävaikutus
Lehdistö/media: Esiintyminen mediassa

Siteeraa tätä

Damskägg, E.-P., Juvela, L., Thuillier, E., & Välimäki, V. (2019). Deep Learning for Tube Amplifier Emulation. teoksessa 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings (Sivut 471-475). Artikkeli 8682805 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2019.8682805

Damskägg, Eero-Pekka ; Juvela, Lauri ; Thuillier, Etienne et al. / Deep Learning for Tube Amplifier Emulation. 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE, 2019. Sivut 471-475 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

@inproceedings{405e0d894477440688046c42e4816a4c,

title = "Deep Learning for Tube Amplifier Emulation",

abstract = "Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities. Faithfully modeling such significant aspect of the original sound in virtual analog software can prove challenging. The current work proposes a generic data-driven approach to virtual analog modeling and applies it to the Fender Bassman 56F-A vacuum-tube amplifier. Specifically, a feedforward variant of the WaveNet deep neural network is trained to carry out a regression on audio waveform samples from input to output of a SPICE model of the tube amplifier. The output signals are pre-emphasized to assist the model at learning the high-frequency content. The results of a listening test suggest that the proposed model accurately emulates the reference device. In particular, the model responds to user control changes, and faithfully restitutes the range of sonic characteristics found across the configurations of the original device.",

keywords = "Integrated circuit modeling, Neural networks, Convolution, Computational modeling, Predictive models, SPICE, Computer architecture, Audio systems, feedforward neural networks, music, nonlinear systems, supervised learning",

author = "Eero-Pekka Damsk{\"a}gg and Lauri Juvela and Etienne Thuillier and Vesa V{\"a}lim{\"a}ki",

year = "2019",

month = may,

day = "1",

doi = "10.1109/ICASSP.2019.8682805",

language = "English",

isbn = "978-1-4799-8132-8",

series = "Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing",

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booktitle = "44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings",

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Damskägg, E-P, Juvela, L , Thuillier, E & Välimäki, V 2019, Deep Learning for Tube Amplifier Emulation. julkaisussa 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings., 8682805, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, Sivut 471-475, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, Iso-Britannia, 12/05/2019. https://doi.org/10.1109/ICASSP.2019.8682805

Deep Learning for Tube Amplifier Emulation. / Damskägg, Eero-Pekka; Juvela, Lauri ; Thuillier, Etienne et al.
44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE, 2019. s. 471-475 8682805 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Deep Learning for Tube Amplifier Emulation

AU - Damskägg, Eero-Pekka

AU - Juvela, Lauri

AU - Thuillier, Etienne

AU - Välimäki, Vesa

N1 - Conference code: 44

PY - 2019/5/1

Y1 - 2019/5/1

N2 - Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities. Faithfully modeling such significant aspect of the original sound in virtual analog software can prove challenging. The current work proposes a generic data-driven approach to virtual analog modeling and applies it to the Fender Bassman 56F-A vacuum-tube amplifier. Specifically, a feedforward variant of the WaveNet deep neural network is trained to carry out a regression on audio waveform samples from input to output of a SPICE model of the tube amplifier. The output signals are pre-emphasized to assist the model at learning the high-frequency content. The results of a listening test suggest that the proposed model accurately emulates the reference device. In particular, the model responds to user control changes, and faithfully restitutes the range of sonic characteristics found across the configurations of the original device.

AB - Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities. Faithfully modeling such significant aspect of the original sound in virtual analog software can prove challenging. The current work proposes a generic data-driven approach to virtual analog modeling and applies it to the Fender Bassman 56F-A vacuum-tube amplifier. Specifically, a feedforward variant of the WaveNet deep neural network is trained to carry out a regression on audio waveform samples from input to output of a SPICE model of the tube amplifier. The output signals are pre-emphasized to assist the model at learning the high-frequency content. The results of a listening test suggest that the proposed model accurately emulates the reference device. In particular, the model responds to user control changes, and faithfully restitutes the range of sonic characteristics found across the configurations of the original device.

KW - Integrated circuit modeling

KW - Neural networks

KW - Convolution

KW - Computational modeling

KW - Predictive models

KW - SPICE

KW - Computer architecture

KW - Audio systems

KW - feedforward neural networks

KW - music

KW - nonlinear systems

KW - supervised learning

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U2 - 10.1109/ICASSP.2019.8682805

DO - 10.1109/ICASSP.2019.8682805

M3 - Conference article in proceedings

SN - 978-1-4799-8132-8

T3 - Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

SP - 471

EP - 475

BT - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings

PB - IEEE

T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 12 May 2019 through 17 May 2019

ER -

Damskägg EP, Juvela L , Thuillier E , Välimäki V. Deep Learning for Tube Amplifier Emulation. julkaisussa 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE. 2019. s. 471-475. 8682805. (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). doi: 10.1109/ICASSP.2019.8682805

Deep Learning for Tube Amplifier Emulation

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Laitteet

Akustiikan laboratorio

Science-IT

Lehtileikkeet

Musiikkiteknologia: Quad Cortex - Pieni laite, oikea soundi

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