911 呼叫分析仪:检测重大紧急情况的重要工具

Paresh Patil, Sushant Gaikwad, Akash Hatkangane
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引用次数: 0

摘要

应急响应系统必须能够及时、准确地评估紧急呼叫。我们在本研究中提供了一种基于机器学习的方法,称为 "911 呼叫分析器",可自动从 911 呼叫录音中识别严重危机。系统使用梅尔频率倒频谱系数(MFCC)提取特征,并使用机器学习和深度学习架构进行分类。为了预测每个紧急呼叫的紧急程度和严重程度,收集到的特征被输入到一个模型中,该模型已在标记呼叫的数据集上经过训练。我们使用测试数据集对 911 呼叫分析仪的性能进行了评估,结果显示 RF 和 XG Boost 模型的准确率为 91%,其次是 SVM,准确率为 90%,CNN,准确率为 69%,最后是 LSTM,准确率为 64%。这些研究结果表明,所建议的方法能够可靠地识别重要危机,从而帮助紧急调度员确定呼叫的优先次序,更合理地分配资源。911 呼叫分析器是一种具有巨大潜力的工具,可提高应急响应系统的效率和效能,最终造福于那些需要帮助的人。关键字911 电话、MFCCs、LSTM、CNN、SVM、RF、XG Boost。
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911 Call Analyzer: A Vital Tool for Detecting Critical Emergencies
Emergency response systems must be able to promptly and accurately evaluate emergency calls. We provide a machine learning- based method in this study, called the "911 Call Analyzer," to automate the process of identifying serious crises from 911 call audio recordings. Mel- frequency cepstral coefficients (MFCCs) are used by the system to extract features, and machine learning and deep learning architectures are used for classification. To forecast the urgency and severity of each emergency call, the collected features are fed into a model that has been trained on a dataset of labelled calls. We assess the 911 Call Analyzer's performance using a test dataset, and we obtain a 91% accuracy rate with RF and XG Boost model followed by SVM with 90% accuracy, CNN with 69% accuracy and lastly LSTM with 64% accuracy. These findings show how well the suggested method works to reliably identify important crises, which helps emergency dispatchers prioritize calls and allocate resources more wisely. The 911 Call Analyzer is a tool that holds great potential for improving emergency response systems' efficacy and efficiency, which will eventually benefit those who are in need. Key Words: 911 calls, MFCCs, LSTM, CNN, SVM, RF, XG Boost.
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