On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis
<|reference_start|>On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis: 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.<|reference_end|>
@article{sarkar2024on,
    title={On the Utility of Speech and Audio Foundation Models for Marmoset Call
  Analysis},
    author={Eklavya Sarkar and Mathew Magimai.-Doss},
    journal={arXiv preprint arXiv:2407.16417},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16417},
    primaryClass={cs.SD cs.LG eess.AS}
}