Classification of producer characteristics in primate long calls using neural networks

Publication Type:Journal Article
Year of Publication:2018
Authors:Robakis, E, Watsa, M, Erkenswick, G
Journal:The Journal of the Acoustical Society of America
Volume:144
Issue:1
Pagination:344 - 353
Date Published:Jan-07-2018
ISSN:0001-4966
Abstract:

Primate long calls are high-amplitude vocalizations that can be critical in maintaining intragroup contact and intergroup spacing, and can encode abundant information about a call's producer, such as age, sex, and individual identity. Long calls of the wild emperor (Saguinus imperator) and saddleback (Leontocebus weddelli) tamarins were tested for these identity signals using artificial neural networks, machine-learning models that reduce subjectivity in vocalization classification. To assess whether modelling could be streamlined by using only factors which were responsible for the majority of variation within networks, each series of networks was re-trained after implementing two methods of feature selection. First, networks were trained and run using only the subset of variables whose weights accounted for ≥50% of each original network's variation, as identified by the networks themselves. In the second, only variables implemented by decision trees in predicting outcomes were used. Networks predicted dependent variables above chance (≥58.7% for sex, ≥69.2 for age class, and ≥38.8% for seven to eight individuals), but classification accuracy was not markedly improved by feature selection. Findings are discussed with regard to implications for future studies on identity signaling in vocalizations and streamlining of data analysis.

URL:http://asa.scitation.org/doi/10.1121/1.5046526
DOI:10.1121/1.5046526
Short Title:The Journal of the Acoustical Society of America
BioAcoustica ID: 
Scratchpads developed and conceived by (alphabetical): Ed Baker, Katherine Bouton Alice Heaton Dimitris Koureas, Laurence Livermore, Dave Roberts, Simon Rycroft, Ben Scott, Vince Smith