Active contour-based detection of estuarine dolphin whistles in spectrogram images

An algorithm for detecting tonal vocalizations from estuarine dolphin (Sotalia guianensis) specimens without interference of a human operator was developed. The raw audio data collected from a passive monitoring sensor in the Cananeia underwater soundscape was converted to spectrogram 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 ridges similar to thick line segments, referred to as line-like, which are adjusted to the geometry of the whistles in the images via an active contour algorithm; third, feature vectors are built from the geometry of each detected whistle, with 9 descriptive features; and fourth, the detections are fed to a random forest classifier to parse out mistakes by the detection process. We developed a system for classifying the characteristic patterns detected as Sotalia guianensis whistles or random empty detections. We obtained accuracy of 0.977 and F1-score of 0.981.