Dolphins and whales use tonal whistles for communication, and it is known that frequency modulation encodes contextual information. An automated mathematical algorithm could characterize the frequency modulation of tonal calls for use with clustering and classification. Most automatic cetacean whistle processing techniques are based on peak or edge detection or require analyst assistance in verifying detections. An alternative paradigm is introduced using techniques of image processing. Frequency information is extracted as ridges in whistle spectrograms. Spectral ridges are the fundamental structure of tonal vocalizations, and ridge detection is a well-established image processing technique, easily applied to vocalization spectrograms. This paradigm is implemented as freely available matlab scripts, coined IPRiT (image processing ridge tracker). Its fidelity in the reconstruction of synthesized whistles is compared to another published whistle detection software package, silbido. Both algorithms are also applied to real-world recordings of bottlenose dolphin (Tursiops trunactus) signature whistles and tested for the ability to identify whistles belonging to different individuals. IPRiT gave higher fidelity and lower false detection than silbido with synthesized whistles, and reconstructed dolphin identity groups from signature whistles, whereas silbido could not. IPRiT appears to be superior to silbido for the extraction of the precise frequency variation of the whistle.
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