Since various phenomena produce infrasound, including both man-made and natural sources,
the ever-growing dataflow demands automatic processes via machine learning for signal
classification. In this study, we demonstrate a single array approach at the Piszkés-tető
(PSZI) infrasound array. Our dataset contains nearly 14,000 manually categorized infrasound
detections, processed with the progressive multi channel correlation (PMCC) algorithm
from three different sources, such as quarry blasts, storms and signals from a power
plant. The dataset was split into a training, a validation and a test subset. Time
and frequency domain features as well as PMCC-related features were extracted. Three
additional PMCC-related features were constructed in a way to measure the similarity
between detections and to be used in single array monitoring. Two different classifiers,
support vector machine and random forest, were used for training. Training was performed
with three-fold cross validation with grid search. The classifiers were tuned on the
training and validation set using the f1 metric (harmonic mean of positive predictive
value and true positive rate). Training, validation and testing were performed with
and without our three new features that measure similarity between the detections
in order to assess their importance in single array monitoring. The selected classifiers
reached f1 scores between 0.88 and 0.93. Our results show a promising step toward
automatic infrasound event classification.
