基于Hilbert-Huang变换的语音合成基音标注搜索新算法
|
摘要
在TD-PSOLA语音合成系统中,基音标注的准确性是一个非常重要的因素。针对传统的短时自相关函数和小波算法准确性不高和没有自适应性的缺点,提出一种基于Hilbert-Huang变换的基音标注方法。该算法通过Hilbert-Huang变换分析语音,具有自适应性,根据自身情况选择基函数,分解过程满足条件自动停止。采用文中自适应算法对整段非平稳语音信号进行基音标注,其中浊音段、过渡段采用Hilbert-Huang变换进行标注,清音段、非语音段用相近基音周期插值。实验证明,相比自相关方法、小波方法,文中算法反映了一帧内准周期的细微变化,适应性较好。对于频率不同的男声和女声,准确度可达到90%以上。
The pitch annotation accuracy is a very important factor in the TD-PSOLA speech synthesis system. In allusion to the shortcomings of low accuracy and lack of adaptability of the traditional short-term auto-correlation function and wavelet algorithm,a pitch annotation method based on Hilbert-Huang transformation is proposed. The algorithm analyzes the speech by means of Hilbert-Huang transformation,has adaptability,and selects basis functions according to its own conditions. The decomposition process of the algorithm stops automatically when the conditions are satisfied. The adaptive algorithm is adopted in this paper to conduct pitch annotation for the whole segment of non-stationary speech signals,in which the voiced speech segment and transition segment are marked by means of Hilbert-Huang transformation,and the voiceless speech segment and non-voice segment are interpolated with the similar pitch period. The experiment shows that in comparison with the auto-correlation method and wavelet method,the algorithm in this paper reflects the subtle variation of the quasi-period in a frame,has good adaptability,and has over 90% of accuracy degree for male and female voices with different frequencies.
The pitch annotation accuracy is a very important factor in the TD-PSOLA speech synthesis system. In allusion to the shortcomings of low accuracy and lack of adaptability of the traditional short-term auto-correlation function and wavelet algorithm,a pitch annotation method based on Hilbert-Huang transformation is proposed. The algorithm analyzes the speech by means of Hilbert-Huang transformation,has adaptability,and selects basis functions according to its own conditions. The decomposition process of the algorithm stops automatically when the conditions are satisfied. The adaptive algorithm is adopted in this paper to conduct pitch annotation for the whole segment of non-stationary speech signals,in which the voiced speech segment and transition segment are marked by means of Hilbert-Huang transformation,and the voiceless speech segment and non-voice segment are interpolated with the similar pitch period. The experiment shows that in comparison with the auto-correlation method and wavelet method,the algorithm in this paper reflects the subtle variation of the quasi-period in a frame,has good adaptability,and has over 90% of accuracy degree for male and female voices with different frequencies.
引文
[1]潘峥嵘,戴芮,张宁.改进的基音周期检测算法研究[J].计算机工程与应用,2015,51(10):223-226.PAN Zhengrong,DAI Rui,ZHANG Ning.Improved algorithm for pitch detection[J].Computer engineering and applications,2015,51(10):223-226.
[2]刘志刚.Hilbert-Huang变换及其在电力系统中的应用[M].北京:科学出版社,2015:32-35.LIU Zhigang.Hilbert-Huang transformation and its application in power system[M].Beijing:Science Press,2015:32-35.
[3]韩芳,王学春,靳宗信.基于ACF的基音检测改进算法[J].现代电子技术,2017,40(19):71-74.HAN Fang,WANG Xuechun,JIN Zongxin.Improved pitch detection algorithm based on ACF[J].Modern electronics technique,2017,40(19):71-74.
[4]ZEREMDINI J,MESSAOUD M A B,BOUZID A.Multiple comb filters and autocorrelation of the multi-scale product for multi-pitch estimation[J].Applied acoustics,2017,120:45-53.
[5]武良丹.基于小波变换与平均幅度差函数倒数的基音周期检测算法[J].科学技术创新,2017(31):114-115.WU Liangdan.Pitch period detection algorithm based on wavelet transform and average amplitude difference function Reciprocal[J].Scientific and technological innovation,2017(31):114-115.
[6]陈盼弟,黄华,何凌.基于自相关和倒谱法的基音检测改进算法[J].计算机应用与软件,2015,32(1):163-166.CHEN Pandi,HUANG Hua,HE Ling.Improved algorithm for pitch detection based on ACF and CEP[J].Computer applications and software,2015,32(1):163-166.
[7]吴兴铨,周金治.基于改进小波变换的语音基音周期检测[J].自动化仪表,2017,38(6):67-70.WU Xingquan,ZHOU Jinzhi.Speech pitch period detection based on improved wavelet transform[J].Process automation instrumentation,2017,38(6):67-70.
[8]王晨,章小兵,刘美娟.提升小波加权自相关函数的基音检测算法[J].应用声学,2018(2):201-207.WANG Chen,ZHANG Xiaobing,LIU Meijuan.Pitch detection based on lifting wavelet transform and weighted autocorrelation[J].Applied acoustics,2018(2):201-207.
[9]孙婷婷,章小兵.一种基于小波包变换加权自相关的基音检测算法[J].计算机工程与科学,2017,39(8):1525-1529.SUN Tingting,ZHANG Xiaobing.A weighted autocorrelation method for pitch detection based on wavelet packet transform[J].Computer engineering and science,2017,39(8):1525-1529.
[10]李德冲.基于改进型Hilbert-Huang变换的旋转机械故障诊断系统设计[D].天津:天津工业大学,2017.LI Dechong.Design of fault diagnosis system for rotating machinery based on improved Hilbert-Huang transform[D].Tianjin:Tianjin Polytechnic University,2017.
[11]杨振,邹男,付进.Hilbert-Huang变换在瞬态信号检测中的应用[J].声学技术,2015,34(2):167-171.YANG Zhen,ZOU Nan,FU Jin.The application of HilbertHuang transform in transient signal detection[J].Technicalacoustics,2015,34(2):167-171.
[12]周小龙,刘薇娜,姜振海,等.一种改进的Hilbert-Huang变换方法及其应用[J].工程科学与技术,2017,49(4):196-204.ZHOU Xiaolong,LIU Weina,JIANG Zhenhai,et al.An improved Hilbert-Huang transform method and its application[J].Advanced engineering sciences,2017,49(4):196-204.
[13]郭会芹.基于Hilbert-Huang的语音信号去噪算法研究[D].长春:长春理工大学,2016.GUO Huiqin.Speech signal denoising algorithm research based on Hilbert-Huang[D].Changchun:Changchun University of Science and Technology,2016.
[14]李乐,王玉英,李小霞.一种改进的小波能量熵语音端点检测算法[J].计算机工程,2017,43(5):268-274.LI Le,WANG Yuying,LI Xiaoxia.An improved wavelet energy entropy algorithm for speech endpoint detection[J].Computer engineering,2017,43(5):268-274.
[2]刘志刚.Hilbert-Huang变换及其在电力系统中的应用[M].北京:科学出版社,2015:32-35.LIU Zhigang.Hilbert-Huang transformation and its application in power system[M].Beijing:Science Press,2015:32-35.
[3]韩芳,王学春,靳宗信.基于ACF的基音检测改进算法[J].现代电子技术,2017,40(19):71-74.HAN Fang,WANG Xuechun,JIN Zongxin.Improved pitch detection algorithm based on ACF[J].Modern electronics technique,2017,40(19):71-74.
[4]ZEREMDINI J,MESSAOUD M A B,BOUZID A.Multiple comb filters and autocorrelation of the multi-scale product for multi-pitch estimation[J].Applied acoustics,2017,120:45-53.
[5]武良丹.基于小波变换与平均幅度差函数倒数的基音周期检测算法[J].科学技术创新,2017(31):114-115.WU Liangdan.Pitch period detection algorithm based on wavelet transform and average amplitude difference function Reciprocal[J].Scientific and technological innovation,2017(31):114-115.
[6]陈盼弟,黄华,何凌.基于自相关和倒谱法的基音检测改进算法[J].计算机应用与软件,2015,32(1):163-166.CHEN Pandi,HUANG Hua,HE Ling.Improved algorithm for pitch detection based on ACF and CEP[J].Computer applications and software,2015,32(1):163-166.
[7]吴兴铨,周金治.基于改进小波变换的语音基音周期检测[J].自动化仪表,2017,38(6):67-70.WU Xingquan,ZHOU Jinzhi.Speech pitch period detection based on improved wavelet transform[J].Process automation instrumentation,2017,38(6):67-70.
[8]王晨,章小兵,刘美娟.提升小波加权自相关函数的基音检测算法[J].应用声学,2018(2):201-207.WANG Chen,ZHANG Xiaobing,LIU Meijuan.Pitch detection based on lifting wavelet transform and weighted autocorrelation[J].Applied acoustics,2018(2):201-207.
[9]孙婷婷,章小兵.一种基于小波包变换加权自相关的基音检测算法[J].计算机工程与科学,2017,39(8):1525-1529.SUN Tingting,ZHANG Xiaobing.A weighted autocorrelation method for pitch detection based on wavelet packet transform[J].Computer engineering and science,2017,39(8):1525-1529.
[10]李德冲.基于改进型Hilbert-Huang变换的旋转机械故障诊断系统设计[D].天津:天津工业大学,2017.LI Dechong.Design of fault diagnosis system for rotating machinery based on improved Hilbert-Huang transform[D].Tianjin:Tianjin Polytechnic University,2017.
[11]杨振,邹男,付进.Hilbert-Huang变换在瞬态信号检测中的应用[J].声学技术,2015,34(2):167-171.YANG Zhen,ZOU Nan,FU Jin.The application of HilbertHuang transform in transient signal detection[J].Technicalacoustics,2015,34(2):167-171.
[12]周小龙,刘薇娜,姜振海,等.一种改进的Hilbert-Huang变换方法及其应用[J].工程科学与技术,2017,49(4):196-204.ZHOU Xiaolong,LIU Weina,JIANG Zhenhai,et al.An improved Hilbert-Huang transform method and its application[J].Advanced engineering sciences,2017,49(4):196-204.
[13]郭会芹.基于Hilbert-Huang的语音信号去噪算法研究[D].长春:长春理工大学,2016.GUO Huiqin.Speech signal denoising algorithm research based on Hilbert-Huang[D].Changchun:Changchun University of Science and Technology,2016.
[14]李乐,王玉英,李小霞.一种改进的小波能量熵语音端点检测算法[J].计算机工程,2017,43(5):268-274.LI Le,WANG Yuying,LI Xiaoxia.An improved wavelet energy entropy algorithm for speech endpoint detection[J].Computer engineering,2017,43(5):268-274.