Revisiting Wright: Improving supervised classification of rat ultrasonic vocalisations using synthetic training data

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.