Acoustic Pattern Recognition Based Digging Detection using Bayesian Network Classifier (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Events happening around us generate many sound signals. Some examples of these incidents are shooting in cities or forested areas, humans chopping wood in forested areas, calling for wild animals or chi birds, talking to vehicles driving in the forest or talking to people illegally crossing a safe border. In these incidents, it is very important to detect the mine activities and their locations, because they indicate illegal intrusion by laying mines or digging holes, placing animal traps in the forest, etc. This paper proposes a method to identify soil dig events in the presence of other forest noise. The sound signal for soil dig is collected by keeping the microphone at different distances from the sound source and digging. Signals were analyzed using spectrogram. A Bayesian Network Classifier is applied to classify the event.