Modeling Time-Variant Responses of Optical Compressors with Selective State Space Models
This work addresses the problem of real-time audio compression emulation for live processing applications, representing an incremental improvement over existing methods.
The paper tackled modeling optical dynamic range compressors for audio processing by using deep neural networks with Selective State Space models, achieving accurate emulation that outperformed state-of-the-art methods on compressors like TubeTech CL 1B and Teletronix LA-2A, with results validated through quantitative metrics and subjective tests.
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.