Version 1
: Received: 25 July 2024 / Approved: 25 July 2024 / Online: 25 July 2024 (14:57:14 CEST)
How to cite:
Takazawa, S. K.; Popenhagen, S. K.; Ocampo Giraldo, L. A.; Hix, J. D.; Thompson, S. J.; Chichester, D. L.; Zeiler, C. P.; Garcés, M. A. Explosion Detection using Smartphones: Ensemble Learning with the Smartphone High-explosive Audio Recordings Dataset and the ESC-50 Dataset. Preprints2024, 2024072087. https://doi.org/10.20944/preprints202407.2087.v1
Takazawa, S. K.; Popenhagen, S. K.; Ocampo Giraldo, L. A.; Hix, J. D.; Thompson, S. J.; Chichester, D. L.; Zeiler, C. P.; Garcés, M. A. Explosion Detection using Smartphones: Ensemble Learning with the Smartphone High-explosive Audio Recordings Dataset and the ESC-50 Dataset. Preprints 2024, 2024072087. https://doi.org/10.20944/preprints202407.2087.v1
Takazawa, S. K.; Popenhagen, S. K.; Ocampo Giraldo, L. A.; Hix, J. D.; Thompson, S. J.; Chichester, D. L.; Zeiler, C. P.; Garcés, M. A. Explosion Detection using Smartphones: Ensemble Learning with the Smartphone High-explosive Audio Recordings Dataset and the ESC-50 Dataset. Preprints2024, 2024072087. https://doi.org/10.20944/preprints202407.2087.v1
APA Style
Takazawa, S. K., Popenhagen, S. K., Ocampo Giraldo, L. A., Hix, J. D., Thompson, S. J., Chichester, D. L., Zeiler, C. P., & Garcés, M. A. (2024). Explosion Detection using Smartphones: Ensemble Learning with the Smartphone High-explosive Audio Recordings Dataset and the ESC-50 Dataset. Preprints. https://doi.org/10.20944/preprints202407.2087.v1
Chicago/Turabian Style
Takazawa, S. K., Cleat P. Zeiler and Milton A. Garcés. 2024 "Explosion Detection using Smartphones: Ensemble Learning with the Smartphone High-explosive Audio Recordings Dataset and the ESC-50 Dataset" Preprints. https://doi.org/10.20944/preprints202407.2087.v1
Abstract
Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially in the local scale as maintaining and deploying networks is costly. With increasing interest in smaller yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosion Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion,” “ambient,” or “other” with true positive rates (recall) greater than 96% for all three categories.
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
