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.
出处
《数据采集与处理》
CSCD
北大核心
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
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
speech endpoint detection
Hilbert-Huang transform
time-frequency analysis
correlation coefficient
threshold criterion
wavelet packet decomposition
