Bayesian Classification of Flight Calls with a Novel Dynamic Time Warping Kernel

Abstract--In this paper we propose a probabilistic classification algorithm with a novel Dynamic Time Warping (DTW) kernel to automatically recognize flight calls of different species of birds. The performance of the method on a real world dataset of warbler (Parulidae) flight calls is competitive to human expert recognition levels and outperforms other classifiers trained on a variety of feature extraction approaches. In addition we offer a novel and intuitive DTW kernel formulation which is positive semi-definite in contrast with previous work. Finally we obtain promising results with a larger dataset of multiple species that we can handle efficiently due to the explicit multiclass probit likelihood of the proposed approach1 .

• Type: Journal
• Year: 2010
• Where: ICMLA
• Authors: Theodoros Damoulas, Samuel Henry, Andrew Farnsworth, Michael Lanzone, Carla Gomes
• Keywords: DTW Kernel Formulation, ICMLA 2010, Machine Learning, Probabilistic Classification Algorithm, Real World Dataset