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Research on Speech Signal’s Blind Separation Using Kalman Filter to Denoise

Author: ZhangJie
Tutor: ZhaoMingWang
School: Wuhan University of Science and Technology
Course: Control Theory and Control Engineering
Keywords: Blind Source Separation Voice signal Filtering GUI Robust feature
CLC: TN912.3
Type: Master's thesis
Year: 2010
Downloads: 88
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Abstract


Blind Source Separation (Blind source separation, BSS) is a need to solve a problem in the signal processing , is a hot research topic emerged in the 1990s , it is in the case of source signals and the transmission channel is almost no available information under the mixed signal from only the observed process of extraction or recovery of the source signal . Priori information is no or very little , making it widely in many areas of traditional signal processing methods can not solve , such as : biomedical , radar and communication , data mining, voice and image analysis , weather analysis , seismic exploration , etc. . Practical application in source signal will be more or less there is a wide variety of noise , blind source separation to solve the signal in the noise environment , will greatly expand the range of applications for blind source separation . This article is based on the idea of blind source separation of voice signals in noisy environments . The main method is in progress before the blind source separation once filtering to achieve enhanced speech signal , undermine the purpose of noise filtering using Kalman filtering method ; then were using the Jade algorithm ( joint approximate diagonalization algorithm the ), Fastica algorithm ( fast independent component analysis algorithm ) to separate the mixed signal , these two algorithms are relatively mature algorithm , has been widely used and recognized . Then use Matlab to design a GUI ( Graphical User Interfaces, Graphical User Interface) to visually demonstrate the entire process . And the speech signal after the separation robust feature extraction (mainly MFCC feature ) , and the separation performance of the two algorithms are based on the results compared . More precisely through the design and simulation of the entire system , the blind source separation of speech signal in the presence of noise environments , blind source separation applications and research have important implications .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing
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