In this paper, we implement analysis and recognition of acoustical sources by the combined use of three techniques, i. e. independent component analysis (ICA), wavelet transformation (WT) and higher-order spectrum (HOS) analysis.
本文联合应用独立分量分析(ICA)、小波变换和高阶谱分析方法,进行噪声源的分析与识别。
参考来源 - 基于小波变换和高阶谱分析的噪声源识别方法研究In this paper, we implement analysis and recognition of acoustical sources by the combined use of three techniques, i. e. independent component analysis (ICA), wavelet transformation (WT) and higher-order spectrum (HOS) analysis.
本文联合应用独立分量分析(ICA)、小波变换和高阶谱分析方法,进行噪声源的分析与识别。
参考来源 - 基于小波变换和高阶谱分析的噪声源识别方法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
用高阶谱分析方法，对肺音信号进行了特征提取。
The lung sound feature were extracted by using higher-order spectral technique.
由分层介质表面两不同点响应互谱分析(SASW)得到的有效相速度并不对应于面波基阶模态相速度，它与波场中高阶模态能量分配比例有关。
Affected by higher Rayleigh wave modes in the surface wave field, the effective phase velocity obtained by spectral analysis of surface waves SASW.
结论作为脑电信号非线性定量分析的一种研究方法，双谱分析可显示出常规脑电分析时无法显示的有价值的高阶信息。
ConclusionBeing a quantitative method of non-Gaussianilty and nonlinearity for EEG study, the bispectral analysis can give more useful high-order information.
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