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Speech/music discrimination by detection: Assessment of time series events using ROC graphs

机译:通过检测识别语音/音乐:使用ROC图评估时间序列事件

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This paper suggests the application of the Receiver Operating Characteristics (ROC) graph to assess the performance of any speech/music discrimination method. ROC graphs are applied in the field of speech/music discrimination to assess the Time Series Events (TSE) method. The discrimination problem is viewed as two detection problems: detection of speech and detection of music. It was found that the optimal feature for detecting speech was silence with a true positive rate of 0.9 and false positive rate of 0.14, whilst the optimal feature for music was non-zero crossing rate NZCR with a true positive rate of 0.71 and false positive rate of 0.08.
机译:本文建议使用接收器工作特性(ROC)图来评估任何语音/音乐歧视方法的性能。 ROC图应用于语音/音乐歧视领域,以评估时间序列事件(TSE)方法。辨别问题被视为两个检测问题:语音检测和音乐检测。结果发现,语音检测的最佳特征是沉默,真实率为0.9,假阳性率为0.14,音乐的最佳特征是非零交叉率NZCR,真实率为0.71,假阳性为零。为0.08。

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