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@INPROCEEDINGS{Sarkar_VIHAR_2024,
         author = {Sarkar, Eklavya and Magimai.-Doss, Mathew},
       keywords = {bandwidth, bioacoustics, call-type and caller classification, speech and audio},
       projects = {Idiap, EVOLANG},
          title = {On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis},
      booktitle = {4th International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots},
           year = {2024},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2024/Sarkar_VIHAR_2024.pdf}
}