Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models

TL;DR

This paper introduces a deep learning approach using structured state space models to accurately and efficiently simulate the nonlinear behavior of analog dynamic range compressors like the Teletronix LA-2A, enabling real-time digital audio processing.

Contribution

The paper presents a novel deep learning model with S4 layers that effectively models analog compressors, offering real-time performance with fewer parameters than previous models.

Findings

Achieves comparable quality to existing models

Runs efficiently in real time

Uses fewer parameters for modeling

Abstract

We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic digital dynamic compressors are potentially useful for many applications, the design process is challenging because the compressors operate nonlinearly over long time scales. Our approach is based on the structured state space sequence model (S4), as implementing the state-space model (SSM) has proven to be efficient at learning long-range dependencies and is promising for modeling dynamic range compressors. We present in this paper a deep learning model with S4 layers to model the Teletronix LA-2A analog dynamic range compressor. The model is causal, executes efficiently in real time, and achieves roughly the same quality as previous deep-learning models but with fewer parameters.