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

• URL: http://arxiv.org/abs/2408.12549v2
• Date: Thu, 29 Aug 2024 09:46:54 GMT
• Title: Modeling Time-Variant Responses of Optical Compressors with Selective State Space Models
• Authors: Riccardo Simionato, Stefano Fasciani,
• Abstract summary: This paper presents a method for modeling optical dynamic range compressors using deep neural networks with Selective State Space models. It features a refined technique integrating Feature-wise Linear Modulation and Gated Linear Units to adjust the network dynamically. The proposed architecture is well-suited for low-latency and real-time applications, crucial in live audio processing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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