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Environmental noise classifier using a new set of feature parameters based on pitch range

机译:使用基于音高范围的一组新特征参数的环境噪声分类器

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Automatic Noise Recognition was performed in two stages: (1) feature extraction based on the pitch range, found by analyzing the autocorrelation function and (2) classification using a classifier trained on the extracted features. Since most environmental noise types change their acoustical characteristics over time, we focused on the "pitch range" of the sounds in order to extract features. Two different classifiers. Support Vector Machines (SVM) and k-means clustering, were performed and compared using the proposed features. The SVM and fc-means clustering classifiers achieve recognition rates up to 95.4% and 92.8%, respectively. Although both classifiers provided high accuracy, the SVM-based classifier outperformed the fc-means clustering classifier byapproximately 7.4%.
机译:自动噪声识别分两个阶段执行:(1)通过分析自相关函数找到基于音高范围的特征提取,以及(2)使用对提取的特征进行训练的分类器进行分类。由于大多数环境噪声类型会随时间改变其声学特性,因此我们集中于声音的“音高范围”以提取特征。两种不同的分类器。使用建议的功能对支持向量机(SVM)和k均值聚类进行了比较。 SVM和fc-means聚类分类器分别实现高达95.4%和92.8%的识别率。尽管两个分类器均提供了较高的准确性,但基于SVM的分类器的性能优于fc-means聚类分类器,约为7.4%。

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