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