关键词: 基音周期, 平均幅度差函数, 经验模式分解, 自相关函数

Abstract: The traditional method of short time Average Magnitude Difference Function（AMDF） often appears the mean downward trend that leads to finding the valleys which are not global lowest point. It also occurs the halving frequency and the doubling frequency errors in pitch tracking. To resolve this problem and enhance the valley value features, an improved algorithm based on AMDF is proposed in this paper. Firstly, the traditional AMDF is decomposed by using Empirical Mode Decomposition（EMD） and reconstructed after removing the trend component to obtain new EMD-AMDF. Secondly, the improved weighted feature of EMDAMDF/ACF is extracted to detect the speech pitch. Finally, experimental results show that this method can increase the valley features of AMDF and improve the accuracy of pitch detection.

Key words: pitch detection, Average Magnitude Difference Function（AMDF）, Empirical Mode Decomposition（EMD）, Auto Correlation Function（ACF）