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.小鼠超声发声（ultrasonic vocalisations，USVs）的部分神经机制，可作为人类言语及言语相关障碍的神经生物学研究的理想模型。当前多数针对发声行为与USVs的研究仅局限于离线分析方法与USVs的监督分类任务，这极大阻碍了新型发声类型的发现以及实时自由行为的相关研究。为解决上述局限，我们开发了AMVOC（Analysis of Mouse VOcal Communication，小鼠发声通信分析）这款免费开源软件，用于USVs的检测与分析。与人工标注的基准真值（ground-truth）USV数据集相比，AMVOC的检测功能（涵盖离线与在线两种模式）具备优异的检测精度，且在噪声环境下的性能优于当前主流方法。此外，AMVOC还集成了一种无监督深度学习方法：通过训练模型并基于卷积自编码器（convolutional autoencoder）提取的隐特征对USVs进行聚类，从而助力USV数据的探索与分析。该聚类结果可通过图形用户界面（Graphical User Interface，GUI）进行可视化展示，支持用户对聚类性能进行评估。上述结果可用于探究个体动物的发声库空间。据此，AMVOC可在更广泛的实验条件下助力发声分析研究，并支持用户开发此前难以实现的小鼠发声行为研究实验设计方案。