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Guitar tone stack modeling with a neural state-space filter
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A4 Artikkeli konferenssijulkaisussa
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en
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6
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Proceedings of the 27th International Conference on Digital Audio Effects (DAFx24), Volume 27, issue 2024, pp. 171-176, Proceedings of the International Conference on Digital Audio Effects
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.
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Sinjanakhom, T, Damskägg, E-P, Mimilakis, S, Gotsopoulos, A & Välimäki, V 2024, Guitar tone stack modeling with a neural state-space filter. in E De Sena & J Mannall (eds), Proceedings of the 27th International Conference on Digital Audio Effects (DAFx24). 2024 edn, vol. 27, 58, Proceedings of the International Conference on Digital Audio Effects, University of Surrey, Guildford, UK, pp. 171-176, International Conference on Digital Audio Effects, Guildford, United Kingdom, 03/09/2024. < https://www.dafx.de/paper-archive/2024/papers/DAFx24_paper_58.pdf >