Mice emit ultrasonic vocalizations (USV) to transmit socially-relevant information. To detect and classify these USVs, here we describe the development of VocalMat. VocalMat is a software that uses image-processing and differential geometry approaches to detect USVs in audio files, eliminating the need for user-defined parameter tuning. VocalMat also uses computational vision and machine learning methods to classify USVs into distinct categories. In a dataset of >4,000 USVs emitted by mice, VocalMat detected more than >98% of the USVs and accurately classified ≈86% of USVs when considering the most likely label out of 11 different USV types. We then used Diffusion Maps and Manifold Alignment to analyze the probability distribution of USV classification among different experimental groups, providing a robust method to quantify and qualify the vocal repertoire of mice. Thus, VocalMat allows accurate and highly quantitative analysis of USVs, opening the opportunity for detailed and high-throughput analysis of this behavior.

中文翻译：

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使用计算机视觉和机器学习对小鼠超声波发声的分析

小鼠发出超声波声（USV）来传递与社会相关的信息。为了检测和分类这些USV，在这里我们描述VocalMat的发展。VocalMat是一款使用图像处理和微分几何方法检测音频文件中USV的软件，无需用户定义参数调整。VocalMat还使用计算视觉和机器学习方法将USV分为不同的类别。考虑到11种不同USV类型中最可能的标签，VocalMat在小鼠发射的> 4,000 USV的数据集中，检测到超过98％的USV，并准确分类了约86％的USV。然后，我们使用扩散图和流形比对分析了不同实验组之间USV分类的概率分布，提供了一种强大的方法来量化和鉴定小鼠的声音库。因此，VocalMat可以对USV进行准确和高度定量的分析，从而为对该行为进行详细的高通量分析提供了机会。