On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis

• URL: http://arxiv.org/abs/2407.16417v2
• Date: Wed, 24 Jul 2024 11:19:22 GMT
• Title: On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis
• Authors: Eklavya Sarkar, Mathew Magimai. -Doss,
• Abstract summary: 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.
• Score: 19.205671029694074
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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