Modeling Analog Dynamic Range Compressors using Deep Learning and
  State-space Models
Hanzhi Yin Gang Cheng Christian J. Steinmetz Ruibin Yuan Richard M. Stern Roger B. Dannenberg
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.