Neural network for multi-exponential sound energy decay analysis
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 20000 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.
Cite
@article{arxiv.2205.09644,
title = {Neural network for multi-exponential sound energy decay analysis},
author = {Georg Götz and Ricardo Falcón Pérez and Sebastian J. Schlecht and Ville Pulkki},
journal= {arXiv preprint arXiv:2205.09644},
year = {2023}
}
Comments
The following article has been submitted to the Journal of the Acoustical Society of America (JASA). After it is published, it will be found at http://asa.scitation.org/journal/jas