Modeling Time-Variant Responses of Optical Compressors with Selective State Space Models
<|reference_start|>Modeling Time-Variant Responses of Optical Compressors with Selective State Space Models: 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, crucial in live audio processing. The method has been validated on the analog optical compressors TubeTech 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 our black-box modeling methods outperform all others, achieving accurate emulation of the compression process for both seen and unseen settings during training. We further show a correlation between this accuracy and the sampling density of the control parameters in the dataset and identify settings with fast attack and slow release as the most challenging to emulate.<|reference_end|>
@article{simionato2024modeling,
    title={Modeling Time-Variant Responses of Optical Compressors with Selective
  State Space Models},
    author={Riccardo Simionato and Stefano Fasciani},
    journal={arXiv preprint arXiv:2408.12549},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12549},
    primaryClass={cs.SD cs.AI eess.AS}
}