Dissertation > Excellent graduate degree dissertation topics show

Endpoint Detection of Speech Signal Based on Empirical Mode Decomposition

Author: ZhaoMing
Tutor: ZhangDeXiang
School: Anhui University
Course: Circuits and Systems
Keywords: Speech Endpoint Detection empirical mode decomposition intrinsic mode functions teager kurtosis
CLC: TN912.3
Type: Master's thesis
Year: 2010
Downloads: 55
Quote: 0
Read: Download Dissertation

Abstract


Speech endpoint detection is the front of speech recognition system, its refers to accurately detect the effective speech signal start and end point from the noise environment, for follow-up processes improve accuracy and save time. Although in a quiet laboratory environment, the accuracy of speech endpoint detection can be satisfactory.But the performance of the detection sharply degrades in the practical different kinds of noise environments. Therefore, in actual different background noise and low SNR conditions of speech endpoint detection technology research has the extremely vital significance.Therefore, speech endpoint detection technology research has the extremely vital significance in actual different background noise and low SNR conditions.The speech signal is non-stationary signals. The empirical mode decomposition (EMD) is a new method for adaptive analysis of non-linear and non-stationary signals. The empirical mode decomposition (EMD) is suited to be used in the nonlinear and non-stationary signal processing and it offers an approach to describe the signal instantaneous characters. This paper applies this method to speech endpoint detection.Firstly, with the analysis of some speech features existing typical algorithms of endpoint detection. Then, two new algorithms of the speech endpoint detection are proposed. The first one:endpoint detection of speech signal based on empirical mode decomposition and energy. Simulation results have shown the performance of the typical algorithm is better than others. The second one: endpoint detection of speech signal based on empirical mode decomposition and teager kurtosis. Simulation results have shown the performance of the typical algorithm is better than others, too.

Related Dissertations

  1. The Recognization of License Plate Based on EMD and Its Application,TP391.41
  2. EMD-based medical image fusion algorithm,TP391.41
  3. Research of Image Edge Detection Based on Wavelet Transform and EMD,TP391.41
  4. EEG Analysis by Using High-Performance Computation,R318.0
  5. Detection and Parameter Estimation of Dsss Based on HHT,TN914.42
  6. The empirical mode decomposition theory its line spectrum of ship radiated noise analysis,TN911.7
  7. Study of Short Term Climatic Prediction Method Based on EMD and Ensemble Prediction,P456
  8. Improved Hilbert-Huang Transform and Its Application in Power System Signal Processing Applications,TN911.7
  9. EMD-based speaker recognition,TN912.34
  10. Study on the Prediction Method for Blast Furnace Gas System Based on Improved Echo State Network,TF321.9
  11. EMD and joint time-frequency analysis of acoustic signals in an array of applications,P631.81
  12. Synthetic aperture radar Doppler characteristics of micro-,TN958
  13. The Research on Tool Fault Diagnosis Based on EMD and SVM,TH165.3
  14. The Fault Diagnosis of Gearbox Base on Hilbert-Huang Transform,TH132.41
  15. Research on Data Analysis and Management of Pulse Signal,TP29-AI
  16. Research of Speech Enhancement Based on Empirical Mode Decomposition,TN912.35
  17. Study on Bearing Fault Diagnosis of Asynchronous Motors Based on Wavelet Packet and EMD,TM307
  18. Signal Detection and Study of Algorithm for Ranging Signal of Sparceborne Laser Rangefinder,V443
  19. Study on Modal Parameters Identification of Structures Based on Hilbert-Huang Transform,TU311.3
  20. The Time-frequency Analysis of Speech Signal Based on Hilbert-Huang Transform,TN912.3

CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing
© 2012 www.DissertationTopic.Net  Mobile