# Neural modeling of magnetic tape recorders

arXiv: https://arxiv.org/abs/2305.16862 · HTML: https://mlsanity.com/paper/2305.16862/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 9 of 9)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Deep Learning for Tube Amplifier Emulation](https://mlsanity.com/paper/1811.00334/) (2018)
- [Adversarial Guitar Amplifier Modelling With Unpaired Data](https://mlsanity.com/paper/2211.00943/) (2022)
- [Realistic Gramophone Noise Synthesis using a Diffusion Model](https://mlsanity.com/paper/2206.06259/) (2022)

## Built on by (most cited first, 3 of 3)

- [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 Time-Dependent Responses of Optical Compressors with Selective State Space Models](https://mlsanity.com/paper/2408.12549/) (2024)

## Abstract (arXiv)

The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today's sound practitioners due to the imperfections embedded in the physics of the magnetic recording process. This paper proposes a method for digitally emulating this character using neural networks. The signal chain of the proposed system consists of three main components: the hysteretic nonlinearity and filtering jointly produced by the magnetic recording process as well as the record and playback amplifiers, the fluctuating delay originating from the tape transport, and the combined additive noise component from various electromagnetic origins. In our approach, the hysteretic nonlinear block is modeled using a recurrent neural network, while the delay trajectories and the noise component are generated using separate diffusion models, which employ U-net deep convolutional neural networks. According to the conducted objective evaluation, the proposed architecture faithfully captures the character of the magnetic tape recorder. The results of this study can be used to construct virtual replicas of vintage sound recording devices with applications in music production and audio antiquing tasks.