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.

中文翻译：

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DeepSqueak：一种基于深度学习的超声波发声检测和分析系统。

啮齿动物通过丰富的超声波发声 (USV) 进行社交交流。USV 的记录和分析在各种行为测试中具有广泛的实用性，并且可以在几乎所有啮齿动物行为模型中无创地执行，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 具有一系列交际功能，但技术和财务限制一直是大多数实验室采用发声分析的障碍。最近，深度学习通过允许计算机执行包括看、听和说在内的类似人类的活动，彻底改变了机器听觉和视觉领域。此类系统由仿生的“深度”人工神经网络构建而成。在这里，我们介绍 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 USV 检测和分类。DeepSqueak 旨在让非专家轻松进入 USV 检测和分析，但具有图形用户界面的灵活性和适应性，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、显着缩短分析时间、优化自动音节分类并对任意大量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。