Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models
This work addresses the problem of digital audio production by providing more efficient models for analog compressors, though it is incremental as it builds on existing deep-learning methods.
The authors tackled the challenge of creating realistic digital models of analog dynamic range compressors by using a deep learning approach with structured state space sequence models (S4), specifically modeling the Teletronix LA-2A compressor to achieve similar quality as previous models but with fewer parameters.
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.