Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to l...Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.展开更多
In order to deal with the non-stationary characteristics of blasting vibration signals and the end issue in the empirical mode decomposition(EMD), an improved endpoint continuation method is proposed. First, the linea...In order to deal with the non-stationary characteristics of blasting vibration signals and the end issue in the empirical mode decomposition(EMD), an improved endpoint continuation method is proposed. First, the linear continuation method of extreme points is used to determine the extremum of the signal endpoint fast. Secondly, the extreme points of transition section outside the signal ends are obtained by a mirror continuation method of extreme points, and then the envelope and continuation curve of the transition section of the signal are constructed. Lastly, the sinusoid of the stationary section outside the signal is constructed to achieve the continuation curve from the transition section to the stationary section. Based on the "singular extreme points" phenomenon of blasting vibration signal, the negative maxima and positive minimum are eliminated, then the maximum and minimum are guaranteed to appear at intervals. Thus,the number of iterations is reduced and the instability of EMD decomposition is improved. The calculation formula of amplitude, cycle and initial phase are given for the transition section and stationary section outside the signal. The endpoint processing effect of the simulated signal and the measured blasting vibration signal show that the improved endpoint continuation method can suppress the signal endpoint effect well.展开更多
In view of the usefulness of Empirical Mode Decomposition （EMD）, Artificial Neural Networks （ ANN）, and Most Relevant Matching Extension （MRME） methods in dealing with nonlinear signals, we pro- pose a new way o...In view of the usefulness of Empirical Mode Decomposition （EMD）, Artificial Neural Networks （ ANN）, and Most Relevant Matching Extension （MRME） methods in dealing with nonlinear signals, we pro- pose a new way of combining these methods to deal with signal prediction. We found the results of combining EMD with either ANN or MRME to have higher prediction precision for a time series than the result of using EMD alone.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 60771033
文摘Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.
基金Supported by the National Natural Science Foundation of China(51374212)
文摘In order to deal with the non-stationary characteristics of blasting vibration signals and the end issue in the empirical mode decomposition(EMD), an improved endpoint continuation method is proposed. First, the linear continuation method of extreme points is used to determine the extremum of the signal endpoint fast. Secondly, the extreme points of transition section outside the signal ends are obtained by a mirror continuation method of extreme points, and then the envelope and continuation curve of the transition section of the signal are constructed. Lastly, the sinusoid of the stationary section outside the signal is constructed to achieve the continuation curve from the transition section to the stationary section. Based on the "singular extreme points" phenomenon of blasting vibration signal, the negative maxima and positive minimum are eliminated, then the maximum and minimum are guaranteed to appear at intervals. Thus,the number of iterations is reduced and the instability of EMD decomposition is improved. The calculation formula of amplitude, cycle and initial phase are given for the transition section and stationary section outside the signal. The endpoint processing effect of the simulated signal and the measured blasting vibration signal show that the improved endpoint continuation method can suppress the signal endpoint effect well.
基金supporteal by the Notional Natural Scince Foundation of Hebei Province(D201000921)
文摘In view of the usefulness of Empirical Mode Decomposition （EMD）, Artificial Neural Networks （ ANN）, and Most Relevant Matching Extension （MRME） methods in dealing with nonlinear signals, we pro- pose a new way of combining these methods to deal with signal prediction. We found the results of combining EMD with either ANN or MRME to have higher prediction precision for a time series than the result of using EMD alone.
基金国家高技术研究发展计划( 863)( the National High- Tech Research and Development Plan of China under Grant No.2007AA04Z116)国家自然科学基金( the National Natural Science Foundation of China under Grant No.70631003)+1 种基金安徽高校省级自然科学研究项目( No.KJ2007B303ZC) 安徽省高校省级自然科学基金( No.KJ2008B107)
