ArticlePloS one
Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations.
Article in PloS one. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Developmental trajectories of vocal behaviors in common marmosets as a reference framework for neurobehavioral studies.Frontiers in neural circuits · 2026Article
- A large annotated dataset of vocalizations by common marmosets.Scientific data · 2025Article
- Beyond bigrams: call sequencing in the common marmoset (Royal Society open science · 2024Article
- Article
- Bioacoustic characterization of the black-and-white ruffed lemur (Varecia variegata) vocal repertoire.Primates; journal of primatology · 2023Article
- The neurobiology of vocal communication in marmosets.Annals of the New York Academy of Sciences · 2023Review
- Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls.PLoS computational biology · 2023Article
- Automated acoustic detection of Geoffroy's spider monkey highlights tipping points of human disturbance.Proceedings. Biological sciences · 2023Article
- Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis.Sensors (Basel, Switzerland) · 2022Article
- Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations.Scientific reports · 2021Article
- A machine learning approach to infant distress calls and maternal behaviour of wild chimpanzees.Animal cognition · 2021Article
- Vocal individuality and rhythm in male and female duet contributions of a nonhuman primate.Current zoology · 2020Article
- Deep convolutional network for animal sound classification and source attribution using dual audio recordings.The Journal of the Acoustical Society of America · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.