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Mean Absorption Coefficient Estimation From Impulse Responses: Deep Learning vs. Sabine

Corto Bastien 1 Antoine Deleforge 1 Cédric Foy 2
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Résumé : We consider the problem of estimating the mean absorp-tion coefficients of a room from an impulse response us-ing supervised learning on simulated training sets. Twoneural network architectures and two training dataset de-signs are considered. The proposed approach is shown toyield smaller estimation errors than the classical Sabineand Eyring formulas, despite not relying on any geomet-rical information on the room. Simulated results demon-strate the robustness of the approach under different chal-lenging acoustic conditions.
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https://hal.inria.fr/hal-03045556
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Submitted on : Tuesday, December 8, 2020 - 9:38:50 AM
Last modification on : Friday, January 14, 2022 - 3:42:11 AM
Long-term archiving on: : Tuesday, March 9, 2021 - 6:32:56 PM

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Corto Bastien, Antoine Deleforge, Cédric Foy. Mean Absorption Coefficient Estimation From Impulse Responses: Deep Learning vs. Sabine. E-FA 2020 - Forum Acusticum 2020, Dec 2020, Lyon / Virtual, France. pp.2, ⟨10.48465/fa.2020.0785⟩. ⟨hal-03045556⟩

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