Modeling Time-Variant Responses of Optical Compressors With Selective State Space Models

Simionato R, Fasciani S. Modeling Time-Variant Responses of Optical Compressors With Selective State Space Models. J Audio Eng Soc. 2025;73(3):144-165. doi:10.17743/jaes.2022.0194

@article{Simionato2025_22808,
  author = {Simionato, Riccardo and Fasciani, Stefano},
  title = {{Modeling Time-Variant Responses of Optical Compressors With Selective State Space Models}},
  journal = {Journal of the Audio Engineering Society},
  volume = {73},
  number = {3},
  pages = {144--165},
  year = {2025},
  month = mar,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0194},
  url = {https://doi.org/10.17743/jaes.2022.0194}
}

TY  - JOUR
TI  - Modeling Time-Variant Responses of Optical Compressors With Selective State Space Models
AU  - Simionato, Riccardo
AU  - Fasciani, Stefano
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 73
IS  - 3
SP  - 144
EP  - 165
PY  - 2025
DA  - 2025/03/07
DO  - 10.17743/jaes.2022.0194
UR  - https://doi.org/10.17743/jaes.2022.0194
PB  - Audio Engineering Society
LA  - en
AB  - This paper presents a method for modeling optical dynamic range compressors using deep neural networks with selective state space models. The proposed approach surpasses previous methods based on recurrent layers by employing a selective state space block to encode the input audio. It features a refined technique integrating feature-wise linear modulation and gated linear units to adjust the network dynamically, conditioning the compression’s attack and release phases according to external parameters. The proposed architecture is well-suited for low-latency and real-time applications, which are crucial in live audio processing. The method has been validated on the analog optical compressors Tube-Tech CL 1B and Teletronix LA-2A, which possess distinct characteristics. Evaluation is performed using quantitative metrics and subjective listening tests, comparing the proposed method with other state-of-the art models. Results show that black-box modeling methods used here outperform all others, achieving accurate emulation of the compression process for both seen and unseen settings during training. Furthermore, it is shown that there is a correlation between this accuracy and the sampling density of the control parameters in the data set and it is identified the settings with fast attack and slow release as the most challenging to emulate.
ER  -