Advanced search
Start date
Betweenand
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

Full text
Author(s):
Turesson, Hjalmar K. ; Ribeiro, Sidarta ; Pereira, Danillo R. ; Papa, Joao P. ; de Albuquerque, Victor Hugo C.
Total Authors: 5
Document type: Journal article
Source: PLoS One; v. 11, n. 9 SEP 21 2016.
Web of Science Citations: 11
Abstract

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. (AU)

FAPESP's process: 15/50319-9 - Meta-heuristic-based optimization of probabilistic neural networks
Grantee:João Paulo Papa
Support type: Regular Research Grants
FAPESP's process: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat
Grantee:Jefferson Antonio Galves
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support type: Regular Research Grants