Automatic detection of estuarine dolphin whistles in spectrogram images

TL;DR

This paper presents an automated system that detects and classifies estuarine dolphin whistles in spectrogram images with high accuracy, reducing human intervention in acoustic monitoring.

Contribution

The authors introduce a novel four-step algorithm combining image processing and machine learning for automatic dolphin whistle detection and classification.

Findings

Achieved approximately 97% classification accuracy.

Developed a robust method combining ridge detection, Hough transform, active contours, and random forests.

Reduced need for manual analysis in acoustic dolphin monitoring.

Abstract

An algorithm for detecting tonal vocalizations from estuarine dolphin (Sotalia guianensis) specimens without interference of a human operator is developed. The raw audio data collected from a passive monitoring sensor in the Canan\'eia underwater soundscape is converted to spectrogram images, containing the desired acoustic event (whistle) as a linear pattern in the images. Detection is a four-step method: first, ridge maps are obtained from the spectrogram images; second, a probabilistic Hough transform algorithm is applied to detect roughly linear ridges, which are adjusted to the true corresponding shape of the whistles via an active contour algorithm; third, feature vectors are built from the geometry of each detected curve; and fourth, the detections are fed to a random forest classifier to parse out false positives. We develop a system capable of reliably classifying roughly 97% of the characteristic patterns detected as Sotalia guianensis whistles or random empty detections.