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Speech processing using the empirical mode decomposition and the Hilbert transform

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Speech processing using the empirical mode decomposition and the Hilbert transform

Gong, Ke (2004) Speech processing using the empirical mode decomposition and the Hilbert transform. Masters thesis, Concordia University.

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Abstract

Huang et al. (1998) [1] proposed a new nonlinear and non-stationary data analysis method based on the empirical mode decomposition (EMD) method, which generates a collection of intrinsic mode functions (IMFs). These IMFs have well-behaved Hilbert transforms, from which the instantaneous frequencies can be calculated. Thus, we can localize any event on the time as well as the frequency axis. As a typical nonlinear and non-stationary data, speech signals are test by the new method. The experiments that use various definitions derived from the EMD and the Hilbert Transform are performed in pitch and formant analysis. The experimental results are compared with the conventional method and some remarks are made. Furthermore, a text-independent speaker identification system also tests the marginal spectrum and the new pitch detection algorithm.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Gong, Ke
Pagination:iv, 64 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science and Software Engineering
Date:2004
Thesis Supervisor(s):Bui, T. D
ID Code:8139
Deposited By:Concordia University Libraries
Deposited On:18 Aug 2011 14:16
Last Modified:18 Aug 2011 15:45
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