On a chirplet transform-based method applied to separating and counting wolf howls

Abstract

We propose a parametric model based on chirp decomposition to modelize wolf chorus howl production. Our aim is counting the number of individuals present in a given recording, task accomplished by phase and amplitude estimation of their corresponding howls. The Chirplet transform is used to obtain a first order approximation of the phase, improving the zero order approximation given by other methods, such as the short time Fourier transform (STFT). This gain in accuracy allows us to use criteria for a more accurate chirp tracking, specially at crossing points in the time–frequency plane, and in the determination of harmonics. We explore the efficiency of the method by applying it to synthetic signals as well as to wolves chorus recordings. Results show good performance for chirp tracking even under strong noise corruption.