基于HHT和OSF的复杂环境语音端点检测

希尔伯特-黄变换是一种全数据驱动的自适应非平稳信号时频分析方法，但是在强噪声环境下语音信号的希尔伯特能量谱曲线波动较大，对语音端点检测造成很大的影响，该文提出了一种基于希尔伯特-黄变换和顺序统计滤波的检测方法。该方法将含噪语音信号进行经验模态分解，通过对固有模态函数进行自适应权重选取获得信号的希尔伯特能量谱，利用顺序统计滤波器对每帧的能量谱进行平滑处理作为语音/非语音的鉴别特征。实验结果表明，该方法适用于复杂噪声环境的端点检测，在低信噪比情况下仍然能够有效地检测出语音信号，降低信号误检率。

Voice Activity Detection in Complex Environment Based on Hilbert-Huang Transform and Order Statistics Filter

Hilbert-Huang Transform(HHT) is a fully data driven adaptive non-stationary signal time-frequency analysis method.But the Hilbert energy spectrum curve of speech signal is fluctuate in strong noise environment,it has a great influence to voice activity detection.So an effective voice activity detection algorithm is proposed based on HHT and Order Statistics Filter(OSF) in this paper.This method first decompose noise signal into intrinsic mode functions by empirical mode decomposition.Then the Hilbert energy spectrum is synthesized by adaptive weight selection of each intrinsic mode functions,through OSF to smooth the energy spectrum.Finally,the speech and noise divergence is judged by means of the smoothed energy spectrum.Experimental results show obviously that under complex noisy environment,this method is still able to effectively detect the speech signal,and reduce the error detection rate in low signal to noise ratio conditions.