  Murine rodents generate ultrasonic vocalizations (USVs) with frequencies that
extend to around 120kHz. These calls are important in social behaviour, and so
their analysis can provide insights into the function of vocal communication,
and its dysfunction. The manual identification of USVs, and subsequent
classification into different subcategories is time consuming. Although machine
learning approaches for identification and classification can lead to enormous
efficiency gains, the time and effort required to generate training data can be
high, and the accuracy of current approaches can be problematic. Here we
compare the detection and classification performance of a trained human against
two convolutional neural networks (CNNs), DeepSqueak and VocalMat, on audio
containing rat USVs. Furthermore, we test the effect of inserting synthetic
USVs into the training data of the VocalMat CNN as a means of reducing the
workload associated with generating a training set. Our results indicate that
VocalMat outperformed the DeepSqueak CNN on measures of call identification,
and classification. Additionally, we found that the augmentation of training
data with synthetic images resulted in a further improvement in accuracy, such
that it was sufficiently close to human performance to allow for the use of
this software in laboratory conditions.
