提升小波加权自相关函数的基音检测算法  

Pitch detection based on lifting wavelet transform and weighted autocorreation

作　　者：王晨 章小兵 刘美娟 

机构地区：[1]安徽工业大学电气与信息工程学院,马鞍山243000

出　　处：《应用声学》2018年第2期201-207,共7页Journal of Applied Acoustics

基　　金：安徽工业大学产学研基金资助重大项目(RD14206003)

摘　　要：随着计算机技术的发展,语音信号处理作为人机交互的重要渠道,其在复杂噪声环境下的特征值检测算法直接关系到计算机的运算效率。基音周期是语音特征值提取的重要参数之一。针对传统基音检测算法在噪声环境下检测精度低的问题,提出了一种基于自适应提升小波变换加权线性预测误差自相关函数的基音检测算法。该方法用多级提升小波近似系数加权求和的方法来弥补自相关函数随着时间延迟量的增加幅值衰减的缺陷;用线性预测误差自相关函数的方法来抑制共振峰的干扰,然后将两种方法结合来突出基音周期处的峰值。实验结果表明,与传统的自相关函数法和小波加权法相比,该方法能有效减弱共振峰的影响,突出基音周期处的峰值,提高基音周期检测精度,鲁棒性更好。With the development of computer technology, speech signal processing as an important skill of human-computer interaction, its feature detection algorithm is directly related to the interaction efficiency,in which the pitch period is one of the most important parameters of speech. Aiming at the problem that the traditional pitch detection algorithm has low detection accuracy in noisy environment, a pitch detection algorithm based on lifting wavelet transform and weighted autocorrelation are proposed. The multi-level lifting wavelet approximation coefficient weighted summation is to compensate the defect that the autocorrelation function decreases with the increase of the amount of time delay, and the linear prediction error autocorrelation function is used to suppress the interference of the formant, then the two methods are combined to achieve a more notable peak at the pitch period. The experimental results show that comparing with the traditional pitch detection algorithm, the method can effectively reduce the influence of the formant, highlight the peak at the pitch period, improve the accuracy of the pitch period detection and make robustness better.

关 键 词：基音检测 提升小波变换 自适应阈值算法 线性预测 自相关函数法 

分 类 号：TN912.3[电子电信—通信与信息系统]