Speech enhancement based on Hilbert-Huang transform

被引：0

作者：

Liu, ZF ^{[1
]}

Liao, ZP ^{[1
]}

Sang, EF ^{[1
]}

机构：

[1] China Earthquake Adm, Inst Engn Mech, Harbin 150001, Peoples R China

来源：

Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9 | 2005年

关键词：

Hilbert-Huang transform; speech enhancement; empirical mode decomposition; intrinsic mode function;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The newly developed Hilbert-Huang Transform (HHT) is introduced briefly in this paper. The HHT method is specially developed for analyzing nonlinear and non-stationary data. The method consists of two parts: (1) the empirical mode decomposition (EMD), and (2) the Hilbert spectral analysis. The EMD, which is the first part of the theory, can decompose any complicated data set into a finite and often small numbers of intrinsic mode functions (IMFs). IMFs also thus admit well-behaved Hilbert transforms. The law of its EMD and characteristics of the IMFs of a speech signal with unwanted sound are studied. Based on these studies, a new noise removal method has been developed. In the application, the HHT has been used to enhance the performance of speech signals by removing unwanted sound.

引用

页码：4908 / 4912

页数：5

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