计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
10期
223-226
,共4页
基音周期%平均幅度差函数%经验模式分解%自相关函数
基音週期%平均幅度差函數%經驗模式分解%自相關函數
기음주기%평균폭도차함수%경험모식분해%자상관함수
pitch detection%Average Magnitude Difference Function(AMDF)%Empirical Mode Decomposition(EMD)%Auto Correlation Function(ACF)
针对传统的短时平均幅度差函数（AMDF）法由于出现均值下降趋势，谷点并非全局最低谷点而导致基音周期提取中的倍频和半频错误出现的情况，提出一种改进算法。将传统的AMDF经过经验模式分解（EMD）处理后去掉趋势项重组新的EMDAMDF，再利用短时自相关函数（ACF）对其进行加权，构造新的EMDAMDF/ACF加权平方特征检测该语音帧的基音。仿真结果表明，该方法有效地加强了AMDF的谷值特性提高了基音周期检测的准确率。
針對傳統的短時平均幅度差函數（AMDF）法由于齣現均值下降趨勢，穀點併非全跼最低穀點而導緻基音週期提取中的倍頻和半頻錯誤齣現的情況，提齣一種改進算法。將傳統的AMDF經過經驗模式分解（EMD）處理後去掉趨勢項重組新的EMDAMDF，再利用短時自相關函數（ACF）對其進行加權，構造新的EMDAMDF/ACF加權平方特徵檢測該語音幀的基音。倣真結果錶明，該方法有效地加彊瞭AMDF的穀值特性提高瞭基音週期檢測的準確率。
침대전통적단시평균폭도차함수（AMDF）법유우출현균치하강추세，곡점병비전국최저곡점이도치기음주기제취중적배빈화반빈착오출현적정황，제출일충개진산법。장전통적AMDF경과경험모식분해（EMD）처리후거도추세항중조신적EMDAMDF，재이용단시자상관함수（ACF）대기진행가권，구조신적EMDAMDF/ACF가권평방특정검측해어음정적기음。방진결과표명，해방법유효지가강료AMDF적곡치특성제고료기음주기검측적준학솔。
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.
