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

TL;DR

This paper introduces a deep neural network approach using Selective State Space models to accurately emulate optical compressors in real-time, outperforming previous methods and adaptable to various compressor types.

Contribution

It presents a novel neural network architecture with Selective State Space blocks and dynamic modulation techniques for modeling optical compressors.

Findings

Outperforms existing models in emulating optical compressors

Effective in real-time, low-latency applications

Identifies challenging parameter settings for modeling

Abstract

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.