A call-independent and automatic acoustic system for the individual recognition of animals: A novel model using four passerines

Research into acoustic recognition systems for animals has focused on call-dependent and species identification rather than call-independent and individual identification. Here we present a system for automatic call-independent individual recognition using mel-frequency cepstral coefficients and Gaussian mixture models across four passerine species. To our knowledge this is the first application of these techniques to the individual recognition of birds, and the results are promising. Accuracies of 89.1-92.5% were achieved and the acoustic feature and classifier method developed here have excellent potential for individual animal recognition and can be easily applied to other species.

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