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Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/55369
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| Title: | 數位音樂典藏之資料探勘與智慧型檢索技術 (II) |
| Other Titles: | Data Mining and Intelligent Retrieval Techniques for Digital Music Archives (II) |
| Authors: | 沈錳坤 |
| Contributors: | 政治大學資訊科學系
行政院國家科學委員會 |
| Keywords: | 數位音樂典藏;資料探勘;智慧型檢索技術 |
| Date: | 2006 |
| Issue Date: | 2012-11-12 11:01:38 (UTC+8) |
| Abstract: | 本計畫的研究重點即為利用音樂探勘技術，研究數位音樂典藏中的智慧型擷取技術。數位音樂典藏的擷取方式包括以後設資料(metadata)查詢、音樂內容擷取, 音樂曲風查詢, 相關回饋, 音樂瀏覽與個人化音樂推薦等，以有助於使用者方便地擷取典藏的數位音樂。在本計畫中，我們主要目的將研究利用音樂探勘中的使用者概念學習(User’s Concept Learning)在相關回饋上，發展以內容主的音樂查詢(Content-based Music Retrieval)技術。傳統的音樂檢索系統主要在提供使用者特定音樂的查詢(target search)。除此之外，使用者也有類型音樂查詢(category search)的需求。在類型音樂查詢中，該類型的所有音都共同具備使用者所定義的概念(semantic concept)。這個由使用者定義的概念在音樂檢索系統上是主觀的且動態產生的。換句話說，同一使用者在不同情境之下對於同一首音樂可能產生不同的解讀概念。為了動態擷取使用者的概念，讓使用者參與在查詢過程的互動機制是必要的。因此， 我們提出將相關回饋(relevance feedback)的機制運用在以內容為主的音樂查詢系統上，讓系統從使用者的相關回饋中學習使用者的概念，並利用這學習出的概念來幫助音樂查詢。由於使用者可能從整首音樂或音樂片段兩種角度來判斷該音樂是否具備使用者定義的概念。因此，本論文提出用以片段為主的音樂模型(segment-based modeling approach)將音樂表示成音樂片段的集合。進一步再從整首音樂和片段中擷取特徵。其次，我們針對該問題提出相關演算法來探勘使用者的概念。該演算法先從相關和不相關的音樂資料庫中個別探勘常見樣式，再利用這些樣式建立分類器以區分音樂的相關性。最後，我們分析各種系統回饋機制對搜尋效果的影響。Most-positive 回傳機制會選擇根據目前系統判斷為最相關的物件。Most-informative 機制則是回傳系統無法判斷其相關性的音樂物件。Most-informative 機制的目的在增加每回合系統從使用者身上得到的資訊量。Hybrid 則是中和前兩種機制的優點。本文中，我們模擬並比較各種回傳機制的效能。實驗結果顯示相關回饋機制確實能提升查詢的效果。
In this project, we investigated the data mining techniques for intelligent retrieval of digital music archive. The way of the digital music archive retrieval, including metadata search, content-based music retrieval, music style retrieval, music browsing, personalized music recommendation and etc., is helpful for retrieving music archive easily. In this project, we utilize the data mining technique to learn user’s concept of relevance feedback for developing content-based music retrieval technique. Traditional content-based music retrieval system retrieves a specific music object which is similar to the user’s query. There is also a need, category search, for retrieving a specific category of music objects. In category search, music objects of the same category share a common semantic concept which is defined by the user. The concept for category search in music retrieval is subjective and dynamic. Different users at different time may have different interpretations for the same music object. In the music retrieval system along with relevance feedback mechanism, users are expected to be involved in the concept learning process. Relevance feedback enables the system to learn user’s concept dynamically. In this project, the relevance feedback mechanism for category search of music retrieval based on the semantic concept learning is investigated. We proposed a segment-based music representation to assist the system in discovering user’s concept in terms of low-level music features. Each music object is modeled as a set of significant motivic patterns (SMP) achieved by discovering motivic repeating pattern. Both global and local music features are considered in concept learning. Moreover, to discover user’s semantic concept, a two-phase frequent pattern mining algorithm is proposed to discover common properties from relevant and irrelevant objects respectively and based on which a classifier is derived for distinguishing music objects. Except user’s feedback, three strategies of the system’s feedback to select objects for user’s relevance judgment are investigated. Most-positive strategy returns the most relevant music object to the user while most-informative strategy returns the most uncertain music objects for improving the discrimination power of the next round. Hybrid feedback strategy returns both of them. Comparative experiments are conducted to evaluate effectiveness of the proposed relevance feedback mechanism. Experimental results show that a better precision can be achieved via proposed relevance feedback mechanism. |
| Relation: | 技術發展
學術補助
研究期間:9503~ 9602
研究經費:997仟元 |
| Data Type: | report |
| Appears in Collections: | [資訊科學系] 國科會研究計畫
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Files in This Item:
| File |
Size | Format | |
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| report13.pdf | 650Kb | Adobe PDF2 | 830 | View/Open |
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