Analysis of ultrasonic vocalizations from mice using computer vision and machine learning.
Analysis of ultrasonic vocalizations from mice using computer vision and machine learning.
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DOI：
10.7554/elife.59161
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发表时间：
2021-03-31
期刊：
影响因子：
7.7
通讯作者：
Dietrich MO
中科院分区：
文献类型：
--
作者：
Fonseca AH;Santana GM;Bosque Ortiz GM;Bampi S;Dietrich MO
老鼠发出超声波发声(USV)，传达与社会相关的信息。为了检测和分类这些USV，我们在这里描述vocalMat。VocalMat是一款使用图像处理和微分几何方法来检测音频文件中的USV的软件，消除了对用户定义参数的需要。VocalMat还使用计算视觉和机器学习方法将无人机分类为不同的类别。在老鼠发射的4000种紫外线的数据集中，≈Mat检测到了98%以上的手动标记紫外线，并从11种紫外线类别中准确地识别了86%的紫外线。然后，我们使用降维工具分析了不同实验组之间USV分类的概率分布，为量化和限定小鼠的发声曲目提供了一种稳健的方法。因此，vocalMat使得在不需要用户输入的情况下对USV执行自动、准确和定量的分析成为可能，从而为这种行为的详细和高通量分析打开了机会。
Mice emit ultrasonic vocalizations (USVs) that communicate socially relevant information. To detect and classify these USVs, here we describe 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 parameters. VocalMat also uses computational vision and machine learning methods to classify USVs into distinct categories. In a data set of >4000 USVs emitted by mice, VocalMat detected over 98% of manually labeled USVs and accurately classified ≈86% of the USVs out of 11 USV categories. We then used dimensionality reduction tools 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 makes it possible to perform automated, accurate, and quantitative analysis of USVs without the need for user inputs, opening the opportunity for detailed and high-throughput analysis of this behavior.