End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models

This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers' sonic response to arbitrary inputs at the full range of controls present on the physical device. While the focus on the paper is on the data collection pipeli...

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Bibliographic Details
Published in:arXiv.org 2024-03
Main Authors: Juvela, Lauri, Eero-Pekka Damskägg, Peussa, Aleksi, Mäkinen, Jaakko, Sherson, Thomas, Mimilakis, Stylianos I, Gotsopoulos, Athanasios
Format: Article
Language:English
Subjects:
Amplifiers
Controllability
Data collection
Guitars
Modelling
Neural networks
Real time
Online Access:Get full text
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Summary:This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers' sonic response to arbitrary inputs at the full range of controls present on the physical device. While the focus on the paper is on the data collection pipeline, we demonstrate the effectiveness of this conditioned black-box approach by training an LSTM model to the task, and comparing its performance to an offline white-box SPICE circuit simulation. Our listening test results demonstrate that the neural amplifier modeling approach can match the subjective performance of a high-quality SPICE model, all while using an automated, non-intrusive data collection process, and an end-to-end trainable, real-time feasible neural network model.
ISSN:2331-8422