Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

Hjalmar K. Turesson, Sidarta Ribeiro, Danillo R. Pereira, João P. Papa, Victor Hugo C. de Albuquerque

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available.

The whole paper is available here.



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