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.

研究通过不同类型的神经网络对超声波声音进行分类，构建了可全自动分析大型数据集并具有高可靠性的流水线，并在鼠类的自闭症样行为研究中发现了关键差异。