Energy-spectrum entropy and subharmonic-harmonic rate methods were used to analyze speech based on speech sinusoidal model.

基于语音正弦模型，采用能量谱熵和子谐波谐波比率方法进行语音分析。

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In the test on driving axle, the 2d spectrum entropy of vibration signals is extracted as the feature indicator and is input into the SVDD classifier.

试验中，提取驱动桥振动信号频谱的二维谱熵作为特征指标，输入到SVDD分类器。

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Then, inputting the two-dimension spectrum entropy into the SVM classification, the result shows that SVM has fine compute efficiency and classified ability.

进一步将二维谱熵作为支持向量机（SVM）分类器的输入量，结果表明SVM具有良好的分类能力和计算效率，可以满足在线监测的要求。

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