DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations
DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations
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DOI：
10.1038/s41386-018-0303-6
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发表时间：
2019-04-01
影响因子：
7.6
通讯作者：
Neumaier, John F.
中科院分区：
文献类型：
--
作者：
Coffey, Kevin R.;Marx, Russell G.;Neumaier, John F.
啮齿动物通过丰富的超声波发声（USV）进行社会交流。USV的记录和分析在各种行为测试中具有广泛的实用性，并且可以在几乎任何啮齿动物行为模型中无创地进行，以提供对测试动物的情绪状态和运动功能的丰富见解。尽管有强有力的证据表明，USV服务于一系列的沟通功能，技术和资金的限制，大多数实验室采用发声分析的障碍。最近，深度学习已经彻底改变了机器听觉和视觉领域，允许计算机执行类似人类的活动，包括看，听和说。这种系统是由仿生的、“深层的”人工神经网络构成的。在这里，我们介绍了DeepSqueak，一个USV检测和分析软件套件，可以使用尖端的区域卷积神经网络架构（Faster-RCNN）自动，快速，可靠地执行人类质量的USV检测和分类。DeepSqueak的设计允许非专家轻松进入USV检测和分析，但灵活且可适应图形用户界面，并提供对众多输入和分析功能的访问。与其他现代程序和手动分析相比，DeepSqueak能够减少误报，提高检测召回率，大大减少分析时间，优化自动音节分类，并对任意数量的音节执行自动语法分析，同时保持手动选择审查和监督分类。DeepSqueak允许将USV记录和分析轻松添加到现有的啮齿动物行为程序中，希望能够揭示广泛的先天反应，以便在与常规结果测量相结合时提供对行为的另一个维度的见解。
Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs). Recording and analysis of USVs has broad utility during diverse behavioral tests and can be performed noninvasively in almost any rodent behavioral model to provide rich insights into the emotional state and motor function of the test animal. Despite strong evidence that USVs serve an array of communicative functions, technical and financial limitations have been barriers for most laboratories to adopt vocalization analysis. Recently, deep learning has revolutionized the field of machine hearing and vision, by allowing computers to perform human-like activities including seeing, listening, and speaking. Such systems are constructed from biomimetic, " deep", artificial neural networks. Here, we present DeepSqueak, a USV detection and analysis software suite that can perform human quality USV detection and classification automatically, rapidly, and reliably using cutting-edge regional convolutional neural network architecture (Faster-RCNN). DeepSqueak was engineered to allow non-experts easy entry into USV detection and analysis yet is flexible and adaptable with a graphical user interface and offers access to numerous input and analysis features. Compared to other modern programs and manual analysis, DeepSqueak was able to reduce false positives, increase detection recall, dramatically reduce analysis time, optimize automatic syllable classification, and perform automatic syntax analysis on arbitrarily large numbers of syllables, all while maintaining manual selection review and supervised classification. DeepSqueak allows USV recording and analysis to be added easily to existing rodent behavioral procedures, hopefully revealing a wide range of innate responses to provide another dimension of insights into behavior when combined with conventional outcome measures.