Study of Empirical Mode Decomposition in Speech Enhancement with Artificial Additive Signal
Date Issued
2009
Date
2009
Author(s)
Chang, Chun-Kai
Abstract
Degradation of the quality of speech caused by the background noise is common in most real situations. How to suppress and remove the noise content in a noisy speech is speech enhancement technique. In traditional signal-channel speech enhancement methods, Wiener filter and spectral subtraction are general methods. But these methods process in frequency domain, the distortion of signal often happen. new signal analyzing method, Hilbert-Huang Transform (HHT), was proposed by Norden E. Huang et al. in 1998. With EMD, signal can be decomposed into a finite number of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. These IMFs with Hilbert transform obtain meaningful instantaneous frequencies. In recent years, EMD was used on speech enhancement. After EMD of white noise, noise component of each IMF can be estimated then remove it. n this thesis, we research on speech enhancement with EMD. After adding an artificial signal to noisy signal, most noise component can concentrate on some IMFs. We can remove most noise by throwing away the IMFs. Adaptive center weighted average filter (ACWA filter) is used to whiten the residual noise in speech. These results of experiment show that the method has good performance of de-noising in low SNR situation and reserve the quality of original speech.
Subjects
Empirical Mode Decomposition
De-noising
HHT
Type
thesis
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