English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6486/11658
Visitors : 23427464      Online Users : 200
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/18377

Title: 應用語音增強法及類神經技術於音樂資料庫之分類
Application of Speech Enhancement and Neural Network Technique for Music Classification in Multimedia System
Authors: 吳建達;鍾丞韋
Contributors: 車輛科技研究所
Keywords: 頻譜減法;歌者辨識;廣義回歸類神經
Eectral subtraction;Singer identification;Generalize regression neural network
Date: 2011-05
Issue Date: 2014-04-29T07:28:36Z
Publisher: 中華民國汽車工程學會
Abstract: 本研究提出一個使用信號頻譜減法增強技術和類神經網路為架構的音樂資料庫的歌手識別分類系統,並期望相關技術可以被應用於車輛之多媒體系統上。信號頻譜減法增強法為一種信號處理技術,廣泛用於消除聲音訊號中的背景噪音。在本研究中,頻譜減法技術應用在含有音樂伴奏為背景噪聲條件下的歌手語音信號增強。在實驗中評估所提出的語音增強算法,頻譜減法、多頻帶頻譜減法和非線性頻譜減法三種不同方法的使用和比較。且於信號分類的步驟,將擷取出的特徵信號作為輸入向量,再以廣義回歸類神經網路(GRNN)進行歌手分類鑑定。實驗結果顯示,本研究所提出的方法對於歌手識別是有效的,也可作為相關後續研究的基礎。
This paper presents a study of a singer identification system using the signal spectral subtraction enhancement technique and artificial neural network. Signal spectral subtraction enhancement is a signal processing technique widely used for eliminating the background noise from sound signals. In the present study, the spectral subtraction technique is applied to the singer signal enhancement with background music. To compare the proposed speech enhancement algorithm, spectral subtraction, multi-band spectral subtraction and non-linear spectral subtraction are used and compared in the experimental investigation. In the signal classification stage, the post-processing features of signals are used as input information to generalize a regression neural network (GRNN) classifier for singer identification. The experimental result indicates the proposed singer identification is effective and has satisfactory performance.
Relation: 車輛工程學刊, 8: 61-73
Appears in Collections:[車輛科技研究所] 期刊論文

Files in This Item:

File SizeFormat
index.html0KbHTML357View/Open


All items in NCUEIR are protected by copyright, with all rights reserved.

 


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback