Acoustilytix™: A Web-Based Automated Ultrasonic Vocalization Scoring Platform.
Acoustilytix™: A Web-Based Automated Ultrasonic Vocalization Scoring Platform.
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DOI：
10.3390/brainsci11070864
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发表时间：
2021-06-29
期刊：
影响因子：
3.3
通讯作者：
Duvauchelle CL
中科院分区：
文献类型：
--
作者：
Ashley CB;Snyder RD;Shepherd JE;Cervantes C;Mittal N;Fleming S;Bailey J;Nievera MD;Souleimanova SI;Nyaoga B;Lichtenfeld L;Chen AR;Maddox WT;Duvauchelle CL
超声发声(USV)反映了情绪处理、大脑神经化学和大脑功能。收集和处理USV数据是手动的、耗时的和昂贵的，这限制了研究人员采用完全有效和细微差别的实验设计的能力，并成为其他研究人员进入的障碍，从而造成了严重的瓶颈。在本报告中，我们简要介绍了基于Web的自动化超声评分工具Acoustilytix™的当前开发和测试情况。Acoustilytix在USV检测和分类过程中实施机器学习方法，并与录音环境无关。我们总结了被USV研究人员识别为所需的用户功能以及这些功能是如何实现的。这些功能包括轻松上传USV文件，以CSV格式输出检测到的USV列表和相关参数，以及手动验证或修改自动检测到的呼叫。在没有用户干预或调整的情况下，Acoustilytix在四个独特的录音环境中实现了93%的灵敏度(衡量Acoustilytix检测真实呼叫的精确度)和73%的精确度(衡量Acoustilytix避免误报的精确度)，并优于流行的DeepSqueak算法(敏感度=88%；精确度=41%)。未来的工作将包括集成和实施基于机器学习的呼叫类型分类预测，该预测将为每个检测到的呼叫向用户推荐一种呼叫类型。呼叫分类准确率目前在71%-79%的准确率范围内，随着更多的USV文件被专家评分，将继续提高准确率，为分类模型提供更多的训练数据。我们还描述了Acoustilytix最近开发的一个功能，它提供了一种快速有效的方式来使用自动学习原则培训手记分员，而不需要专业的手记分员在场，并且建立在学习科学的基础上。关键是让学员练习对数百个电话进行分类，并根据专家的USV分类提供即时纠正反馈。我们表明，这种方法是非常有效的，仅在1000-2000次培训之后，受训者和专家之间的评分者间可靠性(即kappa统计)在0.30-0.75(平均=0.55)之间。最后，我们将简要讨论Acoustilytix平台的未来改进。
Ultrasonic vocalizations (USVs) are known to reflect emotional processing, brain neurochemistry, and brain function. Collecting and processing USV data is manual, time-intensive, and costly, creating a significant bottleneck by limiting researchers’ ability to employ fully effective and nuanced experimental designs and serving as a barrier to entry for other researchers. In this report, we provide a snapshot of the current development and testing of Acoustilytix™, a web-based automated USV scoring tool. Acoustilytix implements machine learning methodology in the USV detection and classification process and is recording-environment-agnostic. We summarize the user features identified as desirable by USV researchers and how these were implemented. These include the ability to easily upload USV files, output a list of detected USVs with associated parameters in csv format, and the ability to manually verify or modify an automatically detected call. With no user intervention or tuning, Acoustilytix achieves 93% sensitivity (a measure of how accurately Acoustilytix detects true calls) and 73% precision (a measure of how accurately Acoustilytix avoids false positives) in call detection across four unique recording environments and was superior to the popular DeepSqueak algorithm (sensitivity = 88%; precision = 41%). Future work will include integration and implementation of machine-learning-based call type classification prediction that will recommend a call type to the user for each detected call. Call classification accuracy is currently in the 71–79% accuracy range, which will continue to improve as more USV files are scored by expert scorers, providing more training data for the classification model. We also describe a recently developed feature of Acoustilytix that offers a fast and effective way to train hand-scorers using automated learning principles without the need for an expert hand-scorer to be present and is built upon a foundation of learning science. The key is that trainees are given practice classifying hundreds of calls with immediate corrective feedback based on an expert’s USV classification. We showed that this approach is highly effective with inter-rater reliability (i.e., kappa statistics) between trainees and the expert ranging from 0.30–0.75 (average = 0.55) after only 1000–2000 calls of training. We conclude with a brief discussion of future improvements to the Acoustilytix platform.
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2
作者：
Basken, Jaime N.;Connor, Nadine P.;Ciucci, Michelle R.
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