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End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models (2403.08559v1)
Published 13 Mar 2024 in cs.SD and eess.AS
Abstract: 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.
- J. Pakarinen and D. T. Yeh, “A review of digital techniques for modeling vacuum-tube guitar amplifiers,” Computer Music J., vol. 33, no. 2, pp. 85–100, 2009.
- “Multiphysics modeling of audio circuits with nonlinear transformers,” Journal of the Audio Engineering Society, vol. 69, no. 6, pp. 374–388, June 2021.
- “Power-balanced modeling of nonlinear coils and transformers for audio circuits,” Journal of the Audio Engineering Society, vol. 69, no. 7/8, pp. 506–516, July 2021.
- “Parallel wave digital filter implementations of audio circuits with multiple nonlinearities,” Journal of the Audio Engineering Society, vol. 70, no. 6, pp. 469–484, June 2022.
- U. Zölzer, Ed., DAFX: Digital Audio Effects, Wiley, second edition, 2011.
- F. Eichas and U. Zölzer, “Black-box modeling of distortion circuits with block-oriented models,” in Proc. DAFx, Brno, Czech Republic, 9 2016, pp. 39–45.
- “Virtual analog modeling of dynamic range compression systems,” in Proc. of the AES 142th Convention, 5 2017.
- F. Eichas and U. Zölzer, “Gray-box modeling of guitar amplifiers,” J. Audio Eng. Soc., vol. 66, no. 12, pp. 1006–1015, 12 2018.
- “Real-time black-box modelling with recurrent neural networks,” in Int. Conference on Digital Audio Effects (DAFx), 2019.
- “Real-time black-box modelling with recurrent neural networks,” in Proc. DAFx, Birmingham, UK, 9 2019, pp. 173–180.
- “Real-time guitar amplifier emulation with deep learning,” Applied Sciences, vol. 10, no. 3, 2 2020.
- T. Schmitz and J.-J. Embrechts, “Nonlinear real-time emulation of a tube amplifier with a long short time memory neural-network,” in Proc. AES 144th Convention, 2018.
- “Deep learning for tube amplifier emulation,” in IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 471–475.
- “Robotic system for controlling audio systems,” Feb. 2021, EU Patent Application No. 1156974.4 - 1207.
- “Robotic system for controlling audio systems,” Feb. 2021, US Patent Application No. 17/669,797.
- N. Christofides, “Worst-case analysis of a new heuristic for the travelling salesman problem,” Tech. Rep., Carnegie-Mellon Univ Pittsburgh Pa Management Sciences Research Group, 1976.
- “Real-time modeling of audio distortion circuits with deep learning,” in Proc. Sound and Music Computing Conference (SMC), Malaga, Spain, 2019, pp. 332–339.
- J. Covert and D. L. Livingston, “A vacuum-tube guitar amplifier model using a recurrent neural network,” in Proc. IEEE Southeastcon, 2013.
- “A vacuum-tube guitar amplifier model using long/short-term memory networks,” in Proc. IEEE SoutheastCon, 2018.
- T. Schmitz and J.-J. Embrechts, “Nonlinear real-time emulation of a tube amplifier with a long short time memory neural-network,” in Audio Engineering Society Convention, 2018.
- “Exposure bias and state matching in recurrent neural network virtual analog models,” in Proc. Int. Conference on Digital Audio Effects (DAFx), 2021, pp. 284–291.
- “Modeling nonlinear audio effects with end-to-end deep neural networks,” in Int. Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 171–175.
- “Lightweight and interpretable neural modeling of an audio distortion effect using hyperconditioned differentiable biquads,” in Proc. ICASSP, 2021, pp. 890–894.
- “Modelling of nonlinear state-space systems using a deep neural network,” in Proc. DAFx, 2019.
- C. Tallec and Y. Ollivier, “Can recurrent neural networks warp time?,” in Proc. Int. Conference on Learning Representations (ICML), Vancouver, Canada, 2018.
- D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. ICLR, 2015.
- N. Koren, “Improved vacuum tube models for SPICE simulations,” Glass Audio, vol. 8, no. 5, pp. 18–27, 1996.
- I.-T. Rec, “P. 800: Methods for subjective determination of transmission quality,” International Telecommunication Union, Geneva, vol. 22, 1996.
- “WebMUSHRA – A comprehensive framework for web-based listening tests,” J. Open Research Software, vol. 6, no. 1, 2 2018.
- I.-T. Rec, “BS. 1770: Algorithms to measure audio programme loudness and true-peak audio level,” International Telecommunication Union, Geneva, 2015.