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Murine rodents generate ultrasonic vocalizations (USVs) with frequencies thatextend to around 120kHz. These calls are important in social behaviour, and sotheir analysis can provide insights into the function of vocal communication,and its dysfunction. The manual identification of USVs, and subsequentclassification into different subcategories is time consuming. Although machinelearning approaches for identification and classification can lead to enormousefficiency gains, the time and effort required to generate training data can behigh, and the accuracy of current approaches can be problematic. Here wecompare the detection and classification performance of a trained human againsttwo convolutional neural networks (CNNs), DeepSqueak and VocalMat, on audiocontaining rat USVs. Furthermore, we test the effect of inserting syntheticUSVs into the training data of the VocalMat CNN as a means of reducing theworkload associated with generating a training set. Our results indicate thatVocalMat outperformed the DeepSqueak CNN on measures of call identification,and classification. Additionally, we found that the augmentation of trainingdata with synthetic images resulted in a further improvement in accuracy, suchthat it was sufficiently close to human performance to allow for the use ofthis software in laboratory conditions.

Utilizing synthetic training data for the supervised classification of rat ultrasonic vocalizations