Rodents communicate through ultrasonic vocalizations (USVs). These calls are of interest because they provide insight into the development and function of vocal communication, and may prove to be useful as a biomarker for dysfunction in models of neurodevelopmental disorders. Rodent USVs can be categorised into different components and while manual classification is time consuming, advances in neural computing have allowed for fast and accurate identification and classification. Here, we adapt a convolutional neural network (CNN), VocalMat, created for analysing mice USVs, for use with rats. We codify a modified schema, adapted from that previously proposed by Wright et al. (2010), for classification, and compare the performance of our adaptation of VocalMat with a benchmark CNN, DeepSqueak. Additionally, we test the effect of inserting synthetic USVs into the training data of our classification network in order to reduce the workload involved in generating a training set. Our results show that the modified VocalMat outperformed the benchmark software on measures of both call identification, and classification. Additionally, we found that the augmentation of training data with synthetic images resulted in a marked improvement in the accuracy of VocalMat when it was subsequently used to analyse novel data. The resulting accuracy on the modified Wright categorizations was sufficiently high to allow for the application of this software in rat USV classification in laboratory conditions. Our findings also show that inserting synthetic USV calls into the training set leads to improvements in accuracy with little extra time-cost.

翻译：啮齿动物通过超声发声（USVs）进行交流。这些叫声具有研究价值，因为它们能揭示发声交流的发展与功能，并可能作为神经发育障碍模型功能障碍的生物标志物。大鼠USV可分为不同组分，虽然人工分类耗时费力，但神经计算的进步已实现快速准确的识别与分类。本研究将用于小鼠USV分析的卷积神经网络（CNN）——VocalMat，适配至大鼠。我们编码了一种改进的分类方案（改编自Wright等人2010年提出的方案），并将VocalMat的适配版本与基准CNN——DeepSqueak的性能进行比较。此外，为减少训练集生成的工作量，我们测试了在分类网络训练数据中插入合成USV的效果。结果表明：改进版VocalMat在叫声识别与分类两项指标上均优于基准软件；同时，用合成图像扩充训练数据后，VocalMat在后续分析新数据时的准确率显著提升。基于改进版Wright分类法的最终准确率足够高，可在实验室条件下将此软件应用于大鼠USV分类。我们的发现还表明：在训练集中插入合成USV叫声能以极低的时间成本提升分类准确率。