Neural network for multi-exponential sound energy decay analysis
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2022-08-12
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Mcode
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Language
en
Pages
13
942-953
942-953
Series
Journal of the Acoustical Society of America, Volume 152, issue 2
Abstract
An established model for sound energy decay functions (EDFs) is the superposition of multiple exponentials and a noise term. This work proposes a neural-network-based approach for estimating the model parameters from EDFs. The network is trained on synthetic EDFs and evaluated on two large datasets of over 20 000 EDF measurements conducted in various acoustic environments. The evaluation shows that the proposed neural network architecture robustly estimates the model parameters from large datasets of measured EDFs while being lightweight and computationally efficient. An implementation of the proposed neural network is publicly available.Description
Keywords
sound energy decay analysis, multi-exponential sound decay, reverberation time, neural network
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Citation
Götz, G, Falcon Perez, R, Schlecht, S & Pulkki, V 2022, ' Neural network for multi-exponential sound energy decay analysis ', Journal of the Acoustical Society of America, vol. 152, no. 2, pp. 942-953 . https://doi.org/10.1121/10.0013416