Species- and individual-specific animal calls can be used in identification as verified in playback experiments and analyses of features extracted from these signals. The use of machine-learning methods and acoustic features borrowed from human speech recognition to identify animals at the species and individual level has increased recently. To date there have been few studies comparing the performances of these methods and features used for call-type-independent species and individual identification. We compared the performance of four machine-learning classifiers in the identification of ten passerine species, and individual identification for three passerines using two acoustic features. The methods did not require us to pre-categorize the component syllables in call-type-independent species and individual identification systems. The results of our experiment indicated that support vector machines (SVM) performed best generally, regardless of which acoustic feature was used, linear predictive coefficients (LPCs) increased the recognition accuracies of hidden Markov models (HMM) greatly, and the most appropriate classifiers for LPCs and Mel-frequency cepstral coefficients (MFCCs) were HMM and SVM respectively. This study will assist researchers in selecting classifiers and features to use in future species and individual recognition studies.
call-type-independent, species identification, individual recognition, machine-learning, Mel-frequency cepstral coefficients, linear predictive coefficients, passerine