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

Bayesian Classification of Flight Calls with a Novel Dynamic Time Warping Kernel,10.1109/ICMLA.2010.69,Theodoros Damoulas,Samuel Henry,Andrew Farnswor

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