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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宏銘 | |
| dc.contributor.author | Yu-Ching Lin | en |
| dc.contributor.author | 林佑璟 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:32:32Z | - |
| dc.date.available | 2011-07-23 | |
| dc.date.copyright | 2009-07-23 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-20 | |
| dc.identifier.citation | [1] A. Agresti. Categorical data analysis. John Wiley and Sons Publications, 2002.
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Learning query-class dependent weights in automatic video retrieval. In Proc. ACM Int. Conf. Multimedia, pages 548–555, 2004. [38] Y.-H. Yang, Y.-C. Lin, H.-T. Cheng, and H. H. Chen. Mr.Emo: Music retrieval in the emotion plane. In Proc. ACM Int. Conf. Multimedia, pages 1003–1004, 2008. [39] Y.-H. Yang, Y.-C. Lin, Y.-F. Su, and H. H. Chen. A regression approach to music emotion recognition. IEEE Trans. Audio, Speech and Language Processing, 16(2):448–457, 2008. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43007 | - |
| dc.description.abstract | 隨著社群標注系統與音樂網路服務的快速增長，獲取大量音樂詮釋資料變得相當容易。由於此類詮釋資料往往與人類對音樂的感知有關，因此可用於幫助基於音樂內容的自動化分類。現有的自動化音樂分類往往受制於音樂訊號特徵與人類感知之間的巨大隔閡，將詮釋資料納入系統，勢必可對分類校能有一定的提昇。在此篇論文中，我們先檢驗情緒與音樂詮釋資料的相關性，接著運用此相關性來幫助音樂情緒的分類。我們提出使用詮釋資料劃分歌曲，並建立特定詮釋資料的分類模型以專注於分類各群內的歌曲情緒。由於一首歌可同時被標上不同型態的音樂詮釋資料，我們再提出了一個新穎的可適性融合的架構，可將各種詮釋資料合併使用。相較於現有方法往往受制於詮釋資料的混亂與稀少，我們提出的方法克服了這些困難並大幅提昇音樂情緒分類的準確度。 | zh_TW |
| dc.description.abstract | Along with the explosive growth of the social tagging systems and musical web services, abundant musical metadata are readily obtainable from the Internet. Since most metadata are related to the human perception of music, they can be utilized to bridge the so-called semantic gap between audio signals and high-level semantics for content-based music classification. In this thesis, we first examine the correlation between emotion and the musical metadta by a statistical association test, and then exploit such correlation for emotion classification. We propose to divide songs according to the metadata and build a metadata-specific model to concentrate on the classification in each group. Since a song can be associated with different types of metadata, such as genre and style, we further propose a novel adaptive fusion scheme to utilize all types of metadata. While the existing methods of exploiting metadata are hampered by the noise and sparseness inherent to metadata, the proposed scheme overcomes these difficulties and significantly improves the accuracy of emotion classification. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:32:32Z (GMT). No. of bitstreams: 1 ntu-98-R96942052-1.pdf: 1618073 bytes, checksum: 6746fa1dc5d509fa10dc5cf0f19c6aa2 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 口試委員會審定書 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
誌謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii 中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii CHAPTER I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Music Emotion Classification . . . . . . . . . . . . . . . . . . . 1 1.2 Metadata Exploitation for Music Classification . . . . . . . . . 2 1.3 Research Contribution . . . . . . . . . . . . . . . . . . . . . . . 2 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . 4 II. Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Metadata and Tag Analysis . . . . . . . . . . . . . . . . . . . . 5 2.2 Metadata Exploitation for Music Classification . . . . . . . . . 7 2.3 Fusion Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 9 III. Data Collection and Analysis . . . . . . . . . . . . . . . . . . . . . 10 3.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Statistical Association Analysis . . . . . . . . . . . . . . . . . . 11 3.3 Metadata Reduction . . . . . . . . . . . . . . . . . . . . . . . . 13 IV. Proposed Framework . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.1 Metadata Exploitation . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 Local Semantic Coherence . . . . . . . . . . . . . . . . . . . . . 19 4.3 Adaptive Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . 21 V. Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . 22 5.2 Evaluation Measures . . . . . . . . . . . . . . . . . . . . . . . . 23 5.3 Metadata Exploitation . . . . . . . . . . . . . . . . . . . . . . . 23 5.4 Adaptive Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.5 Issues About Converting Metadata Based On The Clusters in Section 3.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 VI. Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . 41 BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 | |
| dc.language.iso | en | |
| dc.subject | 音樂情緒分類 | zh_TW |
| dc.subject | 可適性融合 | zh_TW |
| dc.subject | 利用詮釋資料 | zh_TW |
| dc.subject | adaptive fusion | en |
| dc.subject | music emotion classification | en |
| dc.subject | metadata exploitation | en |
| dc.title | 利用詮釋資料分類音樂情緒 | zh_TW |
| dc.title | Exploiting Metadata for Music Emotion Classification | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭士康,徐宏民,蔡偉和 | |
| dc.subject.keyword | 音樂情緒分類,利用詮釋資料,可適性融合, | zh_TW |
| dc.subject.keyword | music emotion classification,metadata exploitation,adaptive fusion, | en |
| dc.relation.page | 44 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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