Deep Learning for Tube Amplifier Emulation

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

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

7 Citations (Scopus)

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.
Original languageEnglish
Title of host publication44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings
PublisherIEEE
Pages471-475
Number of pages5
ISBN (Electronic)978-1-4799-8131-1
ISBN (Print)978-1-4799-8132-8
DOIs
Publication statusPublished - 1 May 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom
Duration: 12 May 201917 May 2019
Conference number: 44

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019

Keywords

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

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  • Cite this

    Damskägg, E-P., Juvela, L., Thuillier, E., & Välimäki, V. (2019). Deep Learning for Tube Amplifier Emulation. In 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings (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