On Feature Learning for Titi Monkey Activity Detection

This paper, a technical summary of our preceding publication, introduces a robust machine learning framework for the detection of vocal activities of Coppery titi monkeys. Utilizing a combination of MFCC features and a bidirectional LSTM-based classifier, we effectively address the challenges posed...

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Bibliographic Details
Published inarXiv.org
Main Authors Ravuri, Aditya, Muir, Jen, Lawrence, Neil D
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.07.2024
Subjects
Classification
Effectiveness
Machine learning
Monkeys
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Summary:This paper, a technical summary of our preceding publication, introduces a robust machine learning framework for the detection of vocal activities of Coppery titi monkeys. Utilizing a combination of MFCC features and a bidirectional LSTM-based classifier, we effectively address the challenges posed by the small amount of expert-annotated vocal data available. Our approach significantly reduces false positives and improves the accuracy of call detection in bioacoustic research. Initial results demonstrate an accuracy of 95\% on instance predictions, highlighting the effectiveness of our model in identifying and classifying complex vocal patterns in environmental audio recordings. Moreover, we show how call classification can be done downstream, paving the way for real-world monitoring.
ISSN:2331-8422