Neural networks were used to classify infrasound data. Two different approaches were compared. One based on the direct classification of time series data, using a custom implementation of the InceptionTime network. For the other approach, we generated 2D images of the wavelet transformation of the signals, which were subsequently classified using a ResNet implementation. Choosing appropriate hyperparameter settings, both achieve a classification accuracy of above 90 %, with the direct approach reaching 95.2 %.
Cite
@article{arxiv.2403.18687,
title = {InceptionTime vs. Wavelet -- A comparison for time series classification},
author = {Daniel Klenkert and Daniel Schaeffer and Julian Stauch},
journal= {arXiv preprint arXiv:2403.18687},
year = {2024}
}