AN AUTOMATED GUNSHOT AUDIO CLASSIFICATION METHOD BASED ON FINGER PATTERN FEATURE GENERATOR AND ITERATIVE RELIEFF FEATURE SELECTOR
AN AUTOMATED GUNSHOT AUDIO CLASSIFICATION METHOD BASED ON FINGER PATTERN FEATURE GENERATOR AND ITERATIVE RELIEFF FEATURE SELECTOR
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发表时间：
2021
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通讯作者：
Araştırma Makalesi;Adıyaman Üniversitesi;M. Dergisi;T. Tuncer;S. Dogan;Erhan Akbal;Emrah Aydemir
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作者：
Araştırma Makalesi;Adıyaman Üniversitesi;M. Dergisi;T. Tuncer;S. Dogan;Erhan Akbal;Emrah Aydemir
音频取证应用和方法对于澄清犯罪非常重要。为了加速音频分析过程并高精度地对音频进行分类，必须将机器学习（ML）方法用于音频取证。提出了一种自动的枪声音频分类方法。为了实现我们的自动枪声分类方法，一个新的枪音频数据集收集从YouTube上的8类在第一阶段。第二阶段提出了一种新的ML方法，该方法包含三个基本阶段。这些相位是一种新的指状模式（finger-pat）、统计矩和离散小波变换（DWT）的特征生成网络，使用迭代ReliefF（IRF）特征选择器进行信息性/区别性特征选择，并使用k最近邻（kNN）分类器（浅）进行分类，以显示通过使用所提出的手指生成和选择的特征的成功。基于pat的特征生成网络和IRF特征选择器。这些方法和kNN取得了94.48%的分类准确率。这些结果表明，我们提出的方法可以用于枪声音频分析。
Audio forensics applications and methods are very crucial to clarify crimes. To accelerate audio analysis process and classify audios with high accuracy, machine learning (ML) methods must be used in audio forensics. An automated gunshot audios classification method is presented in this study. To implement our automated gunshot classification method, a novel gun audios dataset was collected from YouTube with 8 classes in the first phase. A novel ML method is presented in the second phase and the proposed ML method contains three fundamental phases. These phases are a novel finger pattern (finger - pat), statistical moments and discrete wavelet transform (DWT) based feature generation network, informative/distinctive feature selection with iterative ReliefF (IRF) feature selector and classification with a k nearest neighbors (kNN) classifier (shallow) to show success of the generated and selected features by using the proposed finger - pat based feature generation network and IRF feature selector. These methods and kNN achieved 94.48% classification accuracy. These results demonstrate that our proposed method can be used in gunshot audio analysis.