基于EMD与功率谱熵的语音端点检测

为了提高低信噪比下语音端点检测的准确性,提出一种基于经验模态分解与功率谱熵的语音端点检测方法。对带噪语音信号进行经验模态分解获得一系列语音本征模函数,选取功率谱熵作为语音端点检测的特征,并计算特定阶本征模函数的功率谱熵实现语音的端点检测。通过EMD分解可以有效地消除白噪声的影响,仿真结果表明,在低噪比情况下结合经验模态分解和功率谱熵的方法能够有效实现语音端点检测。

Voice Activity Detection Based on EMD and Power Spectrum Entropy

In order to improve the accuracy of voice activity detection in low SNR environments, a new method based on the empirical mode decomposition and power spectrum entropy is proposed to identify speech-segment endpoints. Noisy speech signals are decomposed into a set of intrinsic mode functions, take power spectrum entropy as the feature of voice activity detection, and compute power spectrum entropy of IMFS to achieve voice activity detection. The method of empirical mode decomposition can effectively eliminate the disturbance of additive white Gaussian noises. The simulation results show that combine the methods of power spectrum entropy with empirical mode decomposition can achieve voice activity detection effectively in low signal to noise ratio environments.