Explosion Detection Using Smartphones: Ensemble Learning with the Smartphone High-Explosive Audio Recordings Dataset and the ESC-50 Dataset

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 20; p. 6688
• Main Authors: Takazawa, Samuel K, Popenhagen, Sarah K, Ocampo Giraldo, Luis A, Hix, Jay D, Thompson, Scott J, Chichester, David L, Zeiler, Cleat P, Garcés, Milton A
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17-10-2024; MDPI
• Subjects: Accelerometers; Acoustics; data; Datasets; detection; explosion; Explosions; infrasound; Innovations; Machine learning; Sensors; Smart phones; smartphone; Smartphones; Sound recordings; explosion; detection; smartphone; infrasound; data; machine learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24206688

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 at 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-explosive 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.