CONF Sarkar_VIHAR_2024/IDIAP On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis Sarkar, Eklavya Magimai-Doss, Mathew bandwidth bioacoustics call-type and caller classification speech and audio EXTERNAL http://publications.idiap.ch/attachments/papers/2024/Sarkar_VIHAR_2024.pdf PUBLIC 4th International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots Interspeech 2024 Satellite Event 2024 Marmoset monkeys encode vital information in their calls and serve as a surrogate model for neuro-biologists to understand the evolutionary origins of human vocal communication. Traditionally analyzed with signal processing-based features, recent approaches have utilized self-supervised models pre-trained on human speech for feature extraction, capitalizing on their ability to learn a signal's intrinsic structure independently of its acoustic domain. However, the utility of such foundation models remains unclear for marmoset call analysis in terms of multi-class classification, bandwidth, and pre-training domain. This study assesses feature representations derived from speech and general audio domains, across pre-training bandwidths of 4, 8, and 16 kHz for marmoset call-type and caller classification tasks. Results show that models with higher bandwidth improve performance, and pre-training on speech or general audio yields comparable results, improving over a spectral baseline.