# Efficient neural networks for real-time modeling of analog dynamic range compression

arXiv: https://arxiv.org/abs/2102.06200 · HTML: https://mlsanity.com/paper/2102.06200/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2021.

## Built on by (most cited first, 15 of 15)

- [Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects](https://mlsanity.com/paper/2211.02247/) (2022)
- [DDX7: Differentiable FM Synthesis of Musical Instrument Sounds](https://mlsanity.com/paper/2208.06169/) (2022)
- [Modelling black-box audio effects with time-varying feature modulation](https://mlsanity.com/paper/2211.00497/) (2022)
- [Modulation Extraction for LFO-driven Audio Effects](https://mlsanity.com/paper/2305.13262/) (2023)
- [SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering](https://mlsanity.com/paper/2508.03448/) (2025)
- [Differentiable Grey-box Modelling of Phaser Effects using Frame-based Spectral Processing](https://mlsanity.com/paper/2306.01332/) (2023)
- [Adoption of AI Technology in the Music Mixing Workflow: An Investigation](https://mlsanity.com/paper/2304.03407/) (2023)
- [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)
- [CONMOD: Controllable Neural Frame-based Modulation Effects](https://mlsanity.com/paper/2406.13935/) (2024)
- [Resampling Filter Design for Multirate Neural Audio Effect Processing](https://mlsanity.com/paper/2501.18470/) (2025)
- [Evaluating Neural Networks Architectures for Spring Reverb Modelling](https://mlsanity.com/paper/2409.04953/) (2024)
- [ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications](https://mlsanity.com/paper/2506.12665/) (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)

Deep learning approaches have demonstrated success in modeling analog audio effects. Nevertheless, challenges remain in modeling more complex effects that involve time-varying nonlinear elements, such as dynamic range compressors. Existing neural network approaches for modeling compression either ignore the device parameters, do not attain sufficient accuracy, or otherwise require large noncausal models prohibiting real-time operation. In this work, we propose a modification to temporal convolutional networks (TCNs) enabling greater efficiency without sacrificing performance. By utilizing very sparse convolutional kernels through rapidly growing dilations, our model attains a significant receptive field using fewer layers, reducing computation. Through a detailed evaluation we demonstrate our efficient and causal approach achieves state-of-the-art performance in modeling the analog LA-2A, is capable of real-time operation on CPU, and only requires 10 minutes of training data.