National Changhua University of Education Institutional Repository : Item 987654321/18380

English | 正體中文 | 简体中文 | Items with full text/Total items : 6507/11669
Visitors : 30024424 Online Users : 387

RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope Adv. Search

Login ‧ Upload ‧ Help ‧ About ‧ Administer

NCUEIR > College of Technology > vr > Periodical Articles > Item 987654321/18380

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/18380

Title:	Speaker Identification System Using Empirical Mode Decomposition and An Artificial Neural Network
Authors:	Wu, Jian-Da;Tsai, Yi-Jang
Contributors:	車輛科技研究所
Keywords:	Speaker identification;Empirical mode decomposition;Back-propagation neural network;Generalized regression neural network
Date:	2011-05
Issue Date:	2014-04-29T07:28:39Z
Publisher:	Elsevier Ltd
Abstract:	This paper presents a speaker identification system using empirical mode decomposition (EMD) feature extraction method and artificial neural network in speaker identification. The EMD is an adaptive multi-resolution decomposition technique that appears to be suitable for non-linear, non-stationary data analysis. The EMD sifts the complex signal of time series without losing its original properties and then obtains some useful intrinsic mode function (IMF) components. Calculating the energy of each component can reduce the computation dimensions and enhance the performance of classification. The features were used as inputs to neural network classifiers for speaker identification. In the speaker identification, the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) were applied to verify the performances and the training time in the proposed system. The experimental results indicated the GRNN can achieve better recognition rate performance with feature extraction using the EMD method than BPNN.
Relation:	Expert Systems with Applications, 38(5): 6112-6117
Appears in Collections:	[vr] Periodical Articles

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	669	View/Open

Loading...