Marmosets, with their highly social nature and complex vocal communication system, are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalisations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalisations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalisations of up to 18 marmosets. We optimised the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex, and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21% - 94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalisations but also for analysing vocalisations and tracking vocal learning trajectories of other species.
Dummies, videos and computer animations have been used extensively in animal behaviour to study simple social interactions. These methods allow complete control of one interacting animal, making it possible to test hypotheses about the significance and relevance of different elements of animal displays. Recent studies have demonstrated the potential of videos and interactive displays for studying more complex courtship interactions in the zebra finch, a well-studied songbird. Here, we extended these techniques by developing an animated female zebra finch and showed that ~40% of male zebra finches (n=5/12) sing to this animation. To study real-time social interactions, we developed two possible methods for closed loop control of animations; (1) an arduino based system to initiate videos/animations based on perch hops and (2) a video game engine based system to change animations. Overall, our results provide an important tool for understanding the dynamics of complex social interactions during courtship.
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