一种基于小波包变换加权自相关的基音检测算法

噪声环境下的基音检测在语音信号处理中占有重要地位。为了有效提取低信噪比情况下的语音基音周期,提出了一种基于小波包变换加权线性预测自相关的检测方法。该方法首先利用小波包自适应阈值消除噪声,将多级小波包变换的近似分量求和以突出基音信息,并采用小波包系数加权线性预测误差自相关的方法突出基音周期处的峰值,提高了基音周期检测的精度。实验结果表明,与传统的自相关法、小波加权自相关法相比,该方法鲁棒性好,基音轨迹平滑,具有更高的准确性,即使在信噪比为-5dB时仍能取得较为理想的结果。

A weighted autocorrelation method for pitchdetection based on wavelet packet transform

Pitch detection in a noisy environment plays an important role in speech signal processing. In order to effectively extract the pitch period in low signal to noise ratio (SNR), we propose a weighted auto correlation method based on wavelet packet transform. We employ the wavelet packet adaptive threshold to eliminate noise signals, and use the summation of approximate components after wavelet packet transform to emphasize the pitch information. Then we use the method of linear prediction error autocorrelation function weighted by wavelet packet coefficients to emphasize the peak of the true pitch period. Compared with the traditional methods based on autocorrelation function or wavelet-weighted, the experiments show that the proposed pitch extraction method has higher accuracy and smoother trajectory of pitch period. Moreover, it is robust when the SNR is -5dB.