  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.
