  A key technology in sperm whale (Physeter macrocephalus) monitoring is the
identification of sperm whale communication signals, known as codas. In this
paper we present the first automatic coda detector and annotator. The main
innovation in our detector is graph-based clustering, which utilizes the
expected similarity between the clicks that make up the coda. Results show
detection and accurate annotation at low signal-to-noise ratios, separation
between codas and echolocation clicks, and discrimination between codas from
simultaneously emitting whales. Using this automatic annotator, insights into
the characterization of sperm whale communication are presented. The results
include new types of coda signals, analyzes of the distribution of coda types
among different whales and for different years, and evidence for
synchronization between communicating whales in terms of coda type and coda
transmission time. These results indicate a high degree of complexity in the
communication system of this cetacean species. To ensure traceability, we share
the implementation code of our coda detector.
