Deep Learning for Tube Amplifier Emulation

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

Standard

Deep Learning for Tube Amplifier Emulation. / Damskägg, Eero-Pekka; Juvela, Lauri; Thuillier, Etienne; Välimäki, Vesa.

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. p. 471-475 8682805 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

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

Harvard

Damskägg, E-P, Juvela, L, Thuillier, E & Välimäki, V 2019, Deep Learning for Tube Amplifier Emulation. in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)., 8682805, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, pp. 471-475, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/2019. https://doi.org/10.1109/ICASSP.2019.8682805

APA

Damskägg, E-P., Juvela, L., Thuillier, E., & Välimäki, V. (2019). Deep Learning for Tube Amplifier Emulation. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 471-475). [8682805] (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2019.8682805

Vancouver

Damskägg E-P, Juvela L, Thuillier E, Välimäki V. Deep Learning for Tube Amplifier Emulation. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2019. p. 471-475. 8682805. (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). https://doi.org/10.1109/ICASSP.2019.8682805

Author

Damskägg, Eero-Pekka ; Juvela, Lauri ; Thuillier, Etienne ; Välimäki, Vesa. / Deep Learning for Tube Amplifier Emulation. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. pp. 471-475 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

Bibtex - Download

@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 = "5",
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",
publisher = "IEEE",
pages = "471--475",
booktitle = "ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

}

RIS - Download

TY - GEN

T1 - Deep Learning for Tube Amplifier Emulation

AU - Damskägg, Eero-Pekka

AU - Juvela, Lauri

AU - Thuillier, Etienne

AU - Välimäki, Vesa

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

UR - http://www.scopus.com/inward/record.url?scp=85069003738&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2019.8682805

DO - 10.1109/ICASSP.2019.8682805

M3 - Conference contribution

SN - 978-1-4799-8132-8

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

SP - 471

EP - 475

BT - ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

ER -

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