English  |  正體中文  |  简体中文  |  Items with full text/Total items : 74010/74010 (100%)
Visitors : 24642294      Online Users : 390
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65803

    Title: 基於階層式狄氏程序混合模型之音樂情緒標註之研究;A Study on Hierarchical Dirichlet Process Mixture Model Based Music Emotion Annotation
    Authors: 陳膺任;Ren,Chen-Ying
    Contributors: 資訊工程學系
    Keywords: 階層式狄氏程序;音樂情緒辨識;Hierarchical Dirichlet Process;Music Emotion recognition
    Date: 2014-08-26
    Issue Date: 2014-10-15 17:10:41 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 音樂在人們生活中有著舉足輕重的地位,在資訊數位化且越來越容易保存的現代,
    狄氏程序混合模型(Hierarchical Dirichlet Process Mixture Model)可以共用成分的特性,建
    性可能會造成類別間的混淆,因此我們基於線性鑑別分析(Linear Discriminant Analysis)
    我們也將討論不同的計算測試資料權重方法的差異。;The development of digital technology has enabled the storage of large collections music
    could be. For the convenience of users, some music database applications tag songs with
    some class labels. In tradition, music was classified by artist or genre, but the real influence of
    music is the emotion which it releases. Therefore, researchers have recently been studying the
    music annotation and retrieval method.
    In tradition, the model of each emotion was constructed individually, but an emotion
    cannot be defined clearly in the real world because the classes of emotions are usually
    considered overlapping. Accordingly, this paper proposes an music annotation and retrieval
    system that is based on hierarchical Dirichlet process mixture model (HDPMM), whose
    components can be shared between each model of emotions. Moreover, an improvement in
    HDPMM is proposed by added a discriminant factor to the proposed system based on the
    concept of linear discriminant analysis. The proposed system represents an emotion using a
    weighting coefficient that is related to a global set of components. Moreover, three methods
    are proposed to compute the weighting coefficients of testing data, and using the weighting
    coefficient to determine whether the testing data contain certain emotional content or not.
    Experimental results show that the proposed system performs well in automatic music
    emotion annotation and retrieval tasks. Finally, the evaluation of the three methods for
    computing weighting coefficients of testing data is also discussed in the experiments.
    Appears in Collections:[資訊工程研究所] 博碩士論文

    Files in This Item:

    File Description SizeFormat

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

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明