Journal
PLOS ONE
Volume 11, Issue 9, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0163041
Keywords
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Categories
Funding
- National Council for Scientific and Technological Development [402422/2012-0, 470501/2013-8, 301928/2014-2, 470571/2013-6, 306166/2014-3]
- Sao Paulo Research Foundation [2013/07699-0, 2014/16250-9, 2015/50319-9]
- CNPq/BJT grant [402422/2012-0]
- CNPq [470501/2013-8, 301928/2014-2, 470571/2013-6, 306166/2014-3]
- FAPESP Research, Innovation and Dissemination Center for Neuromathematics (S. Paulo Research Foundation) [2013/07699-0]
- FAPESP [2014/16250-9, 2015/50319-9]
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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 F-1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available.
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