Bayesian inference for latent spectral shapes

This paper proposes a hierarchical model for the analysis of spectrograms of animal calls. The motivation stems from analysing recordings of the so-called grunt calls emitted by various lemur species. Our goal is to identify a latent spectral shape that characterizes each species and facilitates measuring dissimilarities between them. The model addresses the synchronization of animal vocalizations, due to varying time-lengths and speeds, with nonstationary temporal patterns and accounts for periodic sampling artifacts produced by the time discretization of analogue signals. The former is achieved through a synchronization function, and the latter is modelled using a circular representation of time. To overcome the curse of dimensionality inherent in the model’s implementation, we employ the Nearest Neighbour Gaussian Process, and posterior samples are obtained using the Markov chain Monte Carlo method. We apply the model to a real dataset comprising sounds of eight different species. We define a representative sound for each species and compare them using a distance measure. Cross-validation is used to evaluate the predictive capability of our proposal and explore special cases. Additionally, a simulation study is used to demonstrate how effectively the Markov chain Monte Carlo algorithm can identify the parameters used to generate the data.