Discrimination of individual tigers (Panthera tigris) from long distance roars.

This paper investigates the extent of tiger (Panthera tigris) vocal individuality through both qualitative and quantitative approaches using long distance roars from six individual tigers at Omaha's Henry Doorly Zoo in Omaha, NE. The framework for comparison across individuals includes statistical and discriminant function analysis across whole vocalization measures and statistical pattern classification using a hidden Markov model (HMM) with frame-based spectral features comprised of Greenwood frequency cepstral coefficients. Individual discrimination accuracy is evaluated as a function of spectral model complexity, represented by the number of mixtures in the underlying Gaussian mixture model (GMM), and temporal model complexity, represented by the number of sequential states in the HMM. Results indicate that the temporal pattern of the vocalization is the most significant factor in accurate discrimination. Overall baseline discrimination accuracy for this data set is about 70% using high level features without complex spectral or temporal models. Accuracy increases to about 80% when more complex spectral models (multiple mixture GMMs) are incorporated, and increases to a final accuracy of 90% when more detailed temporal models (10-state HMMs) are used. Classification accuracy is stable across a relatively wide range of configurations in terms of spectral and temporal model resolution.