Abstract
In this work, we present a data-driven approach to modeling tone stack circuits in guitar amplifiers and distortion pedals. To this aim, the proposed modeling approach uses a feedforward fully connected neural network to predict the parameters of a coupled-form state-space filter, ensuring the numerical stability of the resulting time-varying system. The neural network is conditioned on the tone controls of the target tone stack and is optimized jointly with the coupled-form state-space filter to match the target frequency response. To assess the proposed approach, we model three popular tone stack schematics with both matched-order and over-parameterized filters and conduct an objective comparison with well-established approaches that use cascaded biquad filters. Results from the conducted experiments demonstrate improved accuracy of the proposed modeling approach, especially in the case of
over-parameterized state-space filters while guaranteeing numerical stability. Our method can be deployed, after training, in real-time audio processors.
over-parameterized state-space filters while guaranteeing numerical stability. Our method can be deployed, after training, in real-time audio processors.
Original language | English |
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Title of host publication | Proceedings of the 27th International Conference on Digital Audio Effects (DAFx24) |
Editors | E. De Sena, J. Mannall |
Place of Publication | Guildford, UK |
Publisher | University of Surrey |
Pages | 171-176 |
Number of pages | 6 |
Volume | 27 |
Edition | 2024 |
Publication status | Published - 3 Sept 2024 |
MoE publication type | A4 Conference publication |
Event | International Conference on Digital Audio Effects - University of Surrey, Guildford, United Kingdom Duration: 3 Sept 2024 → 7 Sept 2024 Conference number: 27 https://dafx24.surrey.ac.uk/ |
Publication series
Name | Proceedings of the International Conference on Digital Audio Effects |
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ISSN (Electronic) | 2413-6689 |
Conference
Conference | International Conference on Digital Audio Effects |
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Abbreviated title | DAFX |
Country/Territory | United Kingdom |
City | Guildford |
Period | 03/09/2024 → 07/09/2024 |
Internet address |
Keywords
- Audio signal processing
- analog circuits
- Digital signal processing
- Machine learning