Analysis of Mouse Vocal Communication (AMVOC): a deep, unsupervised method for rapid detection, analysis and classification of ultrasonic vocalisations

Some aspects of the neural mechanisms underlying mouse ultrasonic vocalisations (USVs) are a useful model for the neurobiology of human speech and speech-related disorders. Much of the research on vocalisations and USVs is limited to offline methods and supervised classification of USVs, hindering the discovery of new types of vocalisations and the study of real-time free behaviour. To address these issues, we developed AMVOC (Analysis of Mouse VOcal Communication) as a free, open-source software to detect and analyse USVs. When compared to hand-annotated ground-truth USV data, AMVOC’s detection functionality (both offline and online) has high accuracy and outperforms leading methods in noisy conditions. AMVOC also includes an unsupervised deep learning approach that facilitates discovery and analysis of USV data by training a model and clustering USVs based on latent features extracted by a convolutional autoencoder. The clustering is visualised in a graphical user interface (GUI) which allows users to evaluate clustering performance. These results can be used to explore the vocal repertoire space of individual animals. In this way, AMVOC will facilitate vocal analyses in a broader range of experimental conditions and allow users to develop previously inaccessible experimental designs for the study of mouse vocal behaviour.