Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models
<|reference_start|>Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models: Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approaches have aimed to automate both the quantitative (detection) and qualitative (classification) analysis of USVs. Here, we present the first systematic evaluation of different types of neural networks for USV classification. We assessed various feedforward networks, including a custom-built, fully-connected network and convolutional neural network, different residual neural networks (ResNets), an EfficientNet, and a Vision Transformer (ViT). Paired with a refined, entropy-based detection algorithm (achieving recall of 94.9% and precision of 99.3%), the best architecture (achieving 86.79% accuracy) was integrated into a fully automated pipeline capable of analyzing extensive USV datasets with high reliability. Additionally, users can specify an individual minimum accuracy threshold based on their research needs. In this semi-automated setup, the pipeline selectively classifies calls with high pseudo-probability, leaving the rest for manual inspection. Our study focuses exclusively on neonatal USVs. As part of an ongoing phenotyping study, our pipeline has proven to be a valuable tool for identifying key differences in USVs produced by mice with autism-like behaviors.<|reference_end|>
@article{herdt2024enhancing,
    title={Enhancing the analysis of murine neonatal ultrasonic vocalizations:
  Development, evaluation, and application of different mathematical models},
    author={Rudolf Herdt, Louisa Kinzel, Johann Georg Maa{ss}, Marvin Walther,
  Henning Fr"ohlich, Tim Schubert, Peter Maass, Christian Patrick Schaaf},
    journal={arXiv preprint arXiv:2405.12957},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12957},
    primaryClass={cs.SD cs.LG eess.AS}
}