Turesson, Hjalmar K.Ribeiro, SidartaPereira, Danillo R. [UNESP]Papa, Joao P. [UNESP]Albuquerque, Victor Hugo C. de2018-11-262018-11-262016-09-21Plos One. San Francisco: Public Library Science, v. 11, n. 9, 14 p., 2016.1932-6203http://hdl.handle.net/11449/161955Automatic 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 F-1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available.14engArtigo10.1371/journal.pone.0163041WOS:000383892700036Acesso abertoWOS000383892700036.pdf