基于小波包分解的含噪语音时频特性分析及端点检测  

Endpoint Detection of Noise-Corrupted Speech Time-Frequency Characteristics Based on Wavelet Packet Decomposition

作　　者：陈金龙[1] 范影乐[1] 倪红霞[1] 武薇[1] 

机构地区：[1]杭州电子科技大学智能控制与机器人研究所,杭州310018

出　　处：《数据采集与处理》2014年第2期293-297,共5页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(60302027)资助项目;浙江省教育厅科研计划(Y201018050)资助项目

摘　　要：针对Hilbert-Huang变换方法在语音处理过程中存在模态混叠问题,本文提出了基于小波包分解的语音时频分析方法。首先对含噪语音进行小波包分解,对各分量分别进行经验模态分解,并运用相关系数阈值准则对固有模态函数进行筛选;然后建立语音信号的Hilbert谱和瞬时能量谱;最后将基于小波包分解的HilbertHuang变换瞬时能量谱方法应用于含噪语音的端点检测。实验结果表明:与传统广义维数以及谱熵算法相比,本文方法具有更好的准确性、稳定性和自适应性,能够有效描述语音信号非线性非平稳的时频特性。To overcome the problem of mode mixing for Hilbert-Huang transform （HHT） in speech processing, a new method of time-frequency analysis based on wavelet packet decompo- sition （WPD） is proposed in this paper. Firstly, noise-corrupted speech is decomposed by u- sing WPD, each component is carried out empirical mode decomposition （EMD） separately, and the intrinsic mode function （IMF） is selected by using correlation threshold criterion. Then, the Hilbert spectrum and instantaneous energy spectrum of speech signal are achieved. Finally, the method of instantaneous energy spectrum based on WPD is applied to noise-cor- rupted speech endpoint detection. Experimental results indicate that the proposed method is more accurate, robust and self-adaptive by comparison with the original generalized dimension （OGD） and the spectral entropy（SE） algorithms. The proposed method can effectively de- scribe the time-frequency characteristics of the non-linear and non-stationary speech signal, and has provided a new idea for the research of speech signal.

关 键 词：语音端点检测 Hilbert—Huang变换 时频分析 相关系数 阈值准则 小波包分解 

分 类 号：TN911.7[电子电信—通信与信息系统]