Recognition of marine sound events using an echo state reservoir and a K-nearest neighbor classifier

Abstract

A sound event detection and classification algorithm was developed to sift through passive acoustic data, and to extract and classify signals of interest. Segments of data containing interesting sound events were identified from raw audio files using framing techniques and standard deviation-based event detection, and statistical acoustic features from the temporal and spectral domains were extracted for each segment. An adapted cyclical reservoir model was employed to accumulate time varying information over the duration of each sound event, evolving raw features to an echo state space representation with a fading temporal memory. A K-Nearest Neighbor classifier was trained on the echo states extracted from a library of recordings corresponding to broad classes of common ambient marine sound types and self-produced noise from autonomous underwater vehicles. A simple majority voting scheme was then utilized to obtain a single representative class label for each sound event. Validation of the classifier using available underwater acoustic libraries and passive acoustic datasets collected using Seagliders equipped with acoustic sensors indicates small improvements in both the per-frame and per-label performance for a model trained on states over one trained on raw features.A sound event detection and classification algorithm was developed to sift through passive acoustic data, and to extract and classify signals of interest. Segments of data containing interesting sound events were identified from raw audio files using framing techniques and standard deviation-based event detection, and statistical acoustic features from the temporal and spectral domains were extracted for each segment. An adapted cyclical reservoir model was employed to accumulate time varying information over the duration of each sound event, evolving raw features to an echo state space representation with a fading temporal memory. A K-Nearest Neighbor classifier was trained on the echo states extracted from a library of recordings corresponding to broad classes of common ambient marine sound types and self-produced noise from autonomous underwater vehicles. A simple majority voting scheme was then utilized to obtain a single representative class label for each sound event. Validation of the classifier us...