# SignalTrain: Profiling Audio Compressors with Deep Neural Networks

arXiv: https://arxiv.org/abs/1905.11928 · HTML: https://mlsanity.com/paper/1905.11928/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 4 of 4)

- [Differentiable Signal Processing With Black-Box Audio Effects](https://mlsanity.com/paper/2105.04752/) (2021)
- [Reverb Conversion of Mixed Vocal Tracks Using an End-to-end Convolutional Deep Neural Network](https://mlsanity.com/paper/2103.02147/) (2021)
- [Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models](https://mlsanity.com/paper/2403.16331/) (2024)
- [WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling](https://mlsanity.com/paper/2507.10534/) (2025)

## Abstract (arXiv)

In this work we present a data-driven approach for predicting the behavior of (i.e., profiling) a given non-linear audio signal processing effect (henceforth "audio effect"). Our objective is to learn a mapping function that maps the unprocessed audio to the processed by the audio effect to be profiled, using time-domain samples. To that aim, we employ a deep auto-encoder model that is conditioned on both time-domain samples and the control parameters of the target audio effect. As a test-case study, we focus on the offline profiling of two dynamic range compression audio effects, one software-based and the other analog. Compressors were chosen because they are a widely used and important set of effects and because their parameterized nonlinear time-dependent nature makes them a challenging problem for a system aiming to profile "general" audio effects. Results from our experimental procedure show that the primary functional and auditory characteristics of the compressors can be captured, however there is still sufficient audible noise to merit further investigation before such methods are applied to real-world audio processing workflows.