Real-time guitar amplifier emulation with deep learning

Alec Wright*, Eero Pekka Damskägg, Lauri Juvela, Vesa Välimäki

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

41 Citations (Scopus)
642 Downloads (Pure)


This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on theWaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for theWaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.

Original languageEnglish
Article number766
Number of pages18
JournalApplied Sciences (Switzerland)
Issue number3
Publication statusPublished - 1 Feb 2020
MoE publication typeA1 Journal article-refereed


  • Acoustic signal processing
  • Audio systems
  • Music
  • Nonlinear systems
  • Signal processing algorithms
  • Supervised learning


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