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Evaluation of Methods for AR Coefficients Estimation Using Monte Carlo Analysis
Klejmova, Eva
Aim of this paper is to give recommendation for work with methods used for estimation of coefficients of autoregressive process. We applied Monte Carlo simulations to investigate performance of Burg, Yule-Walker and covariance methods. Evaluation of precision of spectral estimation is done with focus on signal length and lag order. The results are presented in graphical form and briefly discussed. Taking these results into account, Yule-Walker method shows better performance in case of long length signals and in case of overvalued lag order. Burg and covariance methods provide better results in case of short length signal and undervalued lag order.
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Analysis and comparison of ROC curves of audio signals
Pospíšil, Lukáš ; Staněk, Miroslav (referee) ; Poměnková, Jitka (advisor)
This thesis deals with oportunity of ROC curve usage in the description of methods that work with sound signals. Specifically, it focuses on ways of detecting of stress in speech signals. The detection itselfs is done in a range of frequencies of the sound signal. There is also a classifier designed using ROC curves that decides whether the input signal is stressed or not. The output of this thesis are findings gathered from analyses and also some recommendation based on those analyses.
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Advanced Methods of Audio Signals Interpolation
Pospíšil, Jiří ; Rajmic, Pavel (referee) ; Mach, Václav (advisor)
This diploma thesis deals with the theoretical analysis of the predictive methods of signal interpolation and signal modeling using sinusoidal model. On the basis of this theory the algorithm for the reconstruction of the missing sections in the audio signal is implemented in computing environment MATLAB. Results of mass testing reconstructions are displayed using objective methods SNR and PEMO-Q. Further experiments are carried out on single signals and their evaluation is described.
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Identification of significant spectral components in speach signal in stress
Dulesov, Egor ; Tučková, Jana (referee) ; Poměnková, Jitka (advisor)
The aim of this master’s thesis is to learn the problem of analysis and identification of significant spectral components in speech signal. Based on learning a special literature chooses the suitable methods of spectrum estimate. Does learning the literature in specification of testing of spectral components significate. Makes a procedure for identification of chosen speech formants. Does this procedure for audio signals both of in stress and in normal state. Estimates the results, compares efficiency of chosen methods and determine threshold for chosen formant of analyzed stress signal. States the recommendations for speech spectral analysis in stress situation.
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Evaluation of Methods for AR Coefficients Estimation Using Monte Carlo Analysis
Klejmova, Eva
Aim of this paper is to give recommendation for work with methods used for estimation of coefficients of autoregressive process. We applied Monte Carlo simulations to investigate performance of Burg, Yule-Walker and covariance methods. Evaluation of precision of spectral estimation is done with focus on signal length and lag order. The results are presented in graphical form and briefly discussed. Taking these results into account, Yule-Walker method shows better performance in case of long length signals and in case of overvalued lag order. Burg and covariance methods provide better results in case of short length signal and undervalued lag order.
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Analysis and comparison of ROC curves of audio signals
Pospíšil, Lukáš ; Staněk, Miroslav (referee) ; Poměnková, Jitka (advisor)
This thesis deals with oportunity of ROC curve usage in the description of methods that work with sound signals. Specifically, it focuses on ways of detecting of stress in speech signals. The detection itselfs is done in a range of frequencies of the sound signal. There is also a classifier designed using ROC curves that decides whether the input signal is stressed or not. The output of this thesis are findings gathered from analyses and also some recommendation based on those analyses.
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|
|
Identification of significant spectral components in speach signal in stress
Dulesov, Egor ; Tučková, Jana (referee) ; Poměnková, Jitka (advisor)
The aim of this master’s thesis is to learn the problem of analysis and identification of significant spectral components in speech signal. Based on learning a special literature chooses the suitable methods of spectrum estimate. Does learning the literature in specification of testing of spectral components significate. Makes a procedure for identification of chosen speech formants. Does this procedure for audio signals both of in stress and in normal state. Estimates the results, compares efficiency of chosen methods and determine threshold for chosen formant of analyzed stress signal. States the recommendations for speech spectral analysis in stress situation.
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|
|
Advanced Methods of Audio Signals Interpolation
Pospíšil, Jiří ; Rajmic, Pavel (referee) ; Mach, Václav (advisor)
This diploma thesis deals with the theoretical analysis of the predictive methods of signal interpolation and signal modeling using sinusoidal model. On the basis of this theory the algorithm for the reconstruction of the missing sections in the audio signal is implemented in computing environment MATLAB. Results of mass testing reconstructions are displayed using objective methods SNR and PEMO-Q. Further experiments are carried out on single signals and their evaluation is described.
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