  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.
