Classify different types of boat engine sounds with machine learning

Abstract: When a boat moves in water, it creates a sound with unique features which makes it possible to identify different boat types or even a specific boat. The ability to identify boats is important in the military sector for surveillance purposes.This thesis describes how different audio processing methods and machine learning approaches are implemented, tested and evaluated in order to create a prototype that identifies boats. A total of 87 boat sounds were used and processed in seven different ways. The machine learning approaches Dense Neural Network, Convolutional Neural Network and Recurrent Neural Network were implemented and trained with the processed audio files in order to identify different boat types. Different combinations of audio processing methods and machine learning approaches ability to classify different boat types, were tested with a stratified Kfold test.The result is a prototype with an audio processing method that divides an audio file to equally large segments. Each segment is converted to a logarithmic mel-scaled spectrogram and a delta feature is calculated and added as an extra dimension for each segment. A Convolutional Neural Network is trained with processed audio files and manages to distinguish different boat types with an accuracy of 75%.