摘 要：经验模式分解(EMD)是一种局部的，完全基于数据的自适应信号分解方法，非常适合于分析非平稳、多分量信号。针对经典EMD方法存在模式混淆，容易产生虚假频率分量的不足，该文提出了一种改进的EMD方法。该方法采用高阶极值点信息，通过逆向EMD筛选结果拟合最优包络均值。同时提出了一种基于正交性的筛选停止准则，保证分解结果的合理性。仿真信号和实测语音信号的实验结果证明了该方法的正确性和有效性，采用该方法能有效减小模式混淆，得到较为准确的分解结果。

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Abstract: Empirical mode decomposition (EMD) is a local, fully data driven and self-adaptive analysis approach. It is a powerful tool for analyzing multi-component signals. Aiming at the reduction of scale mixing and artificial frequency components, an improved scheme of EMD is proposed for the analysis and reconstruction of nonstationary and multicomponent speech signals. The improved EMD method uses higher order extrema and then the optimal envelope mean can be obtained by an inverse EMD scheme. A new sifting stop criterion is proposed based on the index of orthogonality. And finally, the mean square error criterion is used for performance evaluation. Experiments on simulation signals and actual sound signals prove the correctness and validity of the modified method. An exact decomposition result can be obtained by the improved EMD method with less scale mixing than the standard EMD method. The proposed method has broad application potential in processing of speech signals, mechanical vibration signals and radar signals, etc.