Wolf chorus howls are complex vocalizations that play an important role in wolves acoustic communication. Most of the vocalizations included in these choruses are harmonic sounds and can be considered as chirp functions (functions with a fundamental frequency that change over time). The chirplet transform for acoustic signals yields an accurate signal approximation of the instantaneous frequencies (IF) and the chirp rates (IF rate of change) of harmonic signals. This allows us to decide if two instantaneous frequencies close in time belong to the same sound. We are testing an algorithm based on the chirplet transform properties for separating multiple voices emitted simultaneously in chirp functions. When a local maximum of the amplitude is found, the algorithm looks for the chirp “track” in the frequency/time domain, considering the instantaneous frequency and chirp rate estimated. With this algorithm, which we call “bloodhound”, we are able to separate multiple voices into voices emitted by single wolves in a chorus context. Besides counting individuals vocalizing simultaneously, this method could be used in measuring acoustic features of different vocalizations automatically, representing an important tool for the study of acoustic communication.