# Deep Learning for Tube Amplifier Emulation

arXiv: https://arxiv.org/abs/1811.00334 · HTML: https://mlsanity.com/paper/1811.00334/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 18 of 18)

- [Modelling black-box audio effects with time-varying feature modulation](https://mlsanity.com/paper/2211.00497/) (2022)
- [Differentiable Signal Processing With Black-Box Audio Effects](https://mlsanity.com/paper/2105.04752/) (2021)
- [Physical Modeling using Recurrent Neural Networks with Fast Convolutional Layers](https://mlsanity.com/paper/2204.10125/) (2022)
- [Differentiable Grey-box Modelling of Phaser Effects using Frame-based Spectral Processing](https://mlsanity.com/paper/2306.01332/) (2023)
- [Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches](https://mlsanity.com/paper/2504.04751/) (2025)
- [SerumRNN: Step by Step Audio VST Effect Programming](https://mlsanity.com/paper/2104.03876/) (2021)
- [Neural modeling of magnetic tape recorders](https://mlsanity.com/paper/2305.16862/) (2023)
- [Comparative Study of State-based Neural Networks for Virtual Analog Audio Effects Modeling](https://mlsanity.com/paper/2405.04124/) (2024)
- [Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models](https://mlsanity.com/paper/2411.14972/) (2024)
- [Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models](https://mlsanity.com/paper/2403.16331/) (2024)
- [Prevailing Research Areas for Music AI in the Era of Foundation Models](https://mlsanity.com/paper/2409.09378/) (2024)
- [Resampling Filter Design for Multirate Neural Audio Effect Processing](https://mlsanity.com/paper/2501.18470/) (2025)
- [White-box Audio VST Effect Programming](https://mlsanity.com/paper/2102.03170/) (2021)
- [Style Transfer for Non-differentiable Audio Effects](https://mlsanity.com/paper/2309.17125/) (2023)
- [Modeling Time-Dependent Responses of Optical Compressors with Selective State Space Models](https://mlsanity.com/paper/2408.12549/) (2024)
- [Anti-aliasing of neural distortion effects via model fine tuning](https://mlsanity.com/paper/2505.11375/) (2025)
- [Parametric Neural Amp Modeling with Active Learning](https://mlsanity.com/paper/2507.02109/) (2025)
- [Parametric Neural Amp Modeling with Active Learning](https://mlsanity.com/paper/2509.26564/) (2025)

## Abstract (arXiv)

Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities. Faithfully modeling such significant aspect of the original sound in virtual analog software can prove challenging. The current work proposes a generic data-driven approach to virtual analog modeling and applies it to the Fender Bassman 56F-A vacuum-tube amplifier. Specifically, a feedforward variant of the WaveNet deep neural network is trained to carry out a regression on audio waveform samples from input to output of a SPICE model of the tube amplifier. The output signals are pre-emphasized to assist the model at learning the high-frequency content. The results of a listening test suggest that the proposed model accurately emulates the reference device. In particular, the model responds to user control changes, and faithfully restitutes the range of sonic characteristics found across the configurations of the original device.