  Sperm whales (Physeter macrocephalus) navigate underwater with a series of
impulsive, click-like sounds known as echolocation clicks. These clicks are
characterized by a multipulse structure (MPS) that serves as a distinctive
pattern. In this work, we use the stability of the MPS as a detection metric
for recognizing and classifying the presence of clicks in noisy environments.
To distinguish between noise transients and to handle simultaneous emissions
from multiple sperm whales, our approach clusters a time series of MPS measures
while removing potential clicks that do not fulfil the limits of inter-click
interval, duration and spectrum. As a result, our approach can handle high
noise transients and low signal-to-noise ratio. The performance of our
detection approach is examined using three datasets: seven months of recordings
from the Mediterranean Sea containing manually verified ambient noise; several
days of manually labelled data collected from the Dominica Island containing
approximately 40,000 clicks from multiple sperm whales; and a dataset from the
Bahamas containing 1,203 labelled clicks from a single sperm whale. Comparing
with the results of two benchmark detectors, a better trade-off between
precision and recall is observed as well as a significant reduction in false
detection rates, especially in noisy environments. To ensure reproducibility,
we provide our database of labelled clicks along with our implementation code.
