The Wavelet Analysis Applied Research, The Pitch Detection Of Speech Signals

In all fields of the digital processing of speech signals, for instance, speech analysis, speech synthesis, speech compression coding, and speech recognition with confirm by speaker, etc, detecting pitch period accurately and reliably is an essential task. It will influence the performance of the whole system seriously.This paper aims at looking for a kind of robust pitch detection algorithm. Based on the achievement of modern phonetics, the generation of speech, its acoustic characteristics and human's sense of hearing are analyzed and characteristics of the waveform of speech signal are acquired. Then, a more systematic analysis and comparison among some typical methods of pitch detection is made. In addition, this paper describes the basic knowledge of wavelet theory in detail, studies the wavelet function, wavelet transform, Multi-resolution analysis, and Mallat algorithm. Finally, we combine wavelet transform, normal autocorrelation, and dynamic programming technology, propose a kind of new pitch detection algorithm. The main idea of the new algorithm is: Firstly, we carry on multistage wavelet transform to original speech signal, deal with the approaching signals on several higher levels by way of weighting and summing, get the synthetic signal with abundant pitch information and stronger periodicity (this result is general and right to the random section of speech including different pitch periods). Secondly, based on the synthetic signal, we adopt the normal autocorrelation method to detect pitch period and deal with the final data by dynamic programming technology.Finish designing program with MATLAB on the computer and carry on the simulation experiment. Experiment's results show that new algorithm has high accuracy in pitch estimate, high speed in operation, good stability, and strong robustness to the noise. It combines the advantage of autocorrelation algorithm and wavelet algorithm, overcomes the phenomena of fractional frequency and double frequency in autocorrelation algorithm, and has better character of resisting the noise than wavelet algorithm. The performance of our new algorithm is obviously superior to the traditional algorithm.