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New deep learning model accurately emulates optical audio compressors

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for accurately modeling the time-dependent responses of optical dynamic range compressors using deep neural networks. This approach leverages Selective State Space models, outperforming previous recurrent layer methods by effectively encoding audio input. The architecture incorporates Feature-wise Linear Modulation and Gated Linear Units to dynamically adjust compression parameters for low-latency applications, demonstrating strong performance on analog compressors like the TubeTech CL 1B and Teletronix LA-2A. AI

IMPACT This research advances deep learning applications in audio processing, potentially leading to more accurate and efficient emulations of analog audio hardware.

RANK_REASON The cluster contains a research paper detailing a new method for modeling audio compressors using deep learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New deep learning model accurately emulates optical audio compressors

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The cluster contains a research paper detailing a new method for modeling audio compressors using deep learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Riccardo Simionato, Stefano Fasciani ·

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

    arXiv:2408.12549v4 Announce Type: replace-cross 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 employ…

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