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研究生:徐嘉連
研究生(外文):Jia-Lien Hsu
論文名稱:內涵式音樂資訊查詢與分析
論文名稱(外文):Content-based Music Information Retrieval and Analysis
指導教授:陳良弼陳良弼引用關係
指導教授(外文):Arbee L.P. Chen
學位類別:博士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:90
語文別:英文
論文頁數:114
中文關鍵詞:內涵式音樂資訊查詢音樂資料庫重覆樣型索引與查詢處理音樂特徵擷取音樂資料分析效能分析主題
外文關鍵詞:content-based music information retrievalmusic databaserepeating patternsindexing and query processingmusic feature extractionmusic data analysisperformance studythemes
相關次數:
  • 被引用被引用:1
  • 點閱點閱:541
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  • 下載下載:140
  • 收藏至我的研究室書目清單書目收藏:5
在本篇論文中我們首先探討內涵式音樂資訊查詢的技術,包括:音樂物件表示法(representation)、相似度衡量(similarity measure)、以及索引和查詢處理。在音樂物件的表示法,我們介紹三種編碼方法(coding scheme)、包括:chord、mubol和music segment,和其相似度衡量的計算式。針對索引和查詢處理的技術,我們歸納suffix tree、n-gram和augmented suffix tree等方法,並進一步做定性的分析和討論。
音樂資訊查詢的各種技術,我們規劃並執行Ultima project,建立一個測試平台(platform)來評估。在這開放式的平台,我們針對各個索引及查詢處理的方法,執行一系列的實驗。特別針對方法的效率(efficiency)和效能(effectiveness),根據定性的討論和定量的實驗數據,整理了關於音樂資訊查詢技術的分析研究報告。
內涵式音樂資訊查詢處理的技術,可以應用在查詢(searching)、分類(classification)、與推薦(recommendation)等方面,其中,我們也深入探討音樂資料的特徵擷取(feature extraction)的問題。在音樂物件中,一段重複出現的音符,我們定義為「重覆樣型(repeating pattern)」。重覆樣型是音樂物件中的一項重要特徵。例如,樂曲中的「主題」就是重覆樣型。針對如何在音樂物件中找出重覆樣型的問題,我們提出兩個方法。在第一個方法,我們設計correlative matrix的資料結構和演算法,能夠有效率地擷取音樂物件中的重覆樣型。在第二個方法,我們定義string-join的方法和RP-tree的資料結構,也能夠有效率地擷取重覆樣型。同時,我們也做實作這兩個方法,並就效率和效能的方面來做分析、比較。
更進一步的,從擷取重覆樣型的問題,延伸到擷取「相似重覆樣型(approximate repeating pattern)」。我們介紹兩個在序列資料(sequence data)中擷取相似重覆樣型的應用。根據三種相似類型(包括:longer_length、shorter_length和equal_length),我們明確地定義了相似重覆樣型的問題。其中,針對longer_length這類型問題,我們利用cut和pattern_join的方法、提出一演算法來解決這問題。另外,特別針對長的重覆樣型(long pattern),我們利用generalized_pattern_join的方法,能更有效率在序列資料中擷取長的重覆樣型。同樣的,我們也以實作來驗證這演算法的效率。
In this thesis, we first discuss the techniques used in content-based music information retrieval. The techniques include the methods to represent music objects, the similarity measures of music objects, and indexing and query processing for music object retrieval. To represent music objects, we introduce three coding schemes, i.e., chord, mubol, and music segment. Various similarity measures are then presented, followed by various index structures and the associated query processing algorithms. The index structures include suffix tree, n-gram, and augmented suffix tree. A qualitative comparison of these techniques is finally performed to show the intrinsic difficulty of the problem of content-based music information retrieval.
We also initiate the Ultima project which aims to construct a platform for evaluating various approaches of music information retrieval. Three approaches with the corresponding tree-based, list-based, and (n-gram+tree)-based index structures are implemented. A series of experiments has been carried out. With the support of the experiment results, we compare the performance of index construction and query processing of the three approaches and give a summary for efficient content-based music information retrieval.
The feature extraction problem for music objects is also studied to support content-based music information retrieval in searching, classification, recommendation, and so forth. A repeating pattern in music data is defined as a sequence of notes which appears more than once in a music object. The themes are a typical kind of repeating patterns. The themes and other non-trivial repeating patterns are important music features which can be used for both content-based retrieval of music data and music data analysis. We propose two approaches for fast discovering non-trivial repeating patterns in music objects. In the first approach, we develop a data structure called correlative matrix and its associated algorithms for extracting the repeating patterns. In the second approach, we introduce a string-join operation and a data structure called RP-tree for the same purpose. Experiments are performed to compare these two approaches with others. The results are also analyzed to show the efficiency and the effectiveness of our approaches.
Further, we extend the problem of finding exact repeating patterns to the one of finding approximate repeating patterns. First, two applications are introduced to motivate our research of finding approximate repeating patterns from sequence data. An approximate repeating pattern is defined as a sequence of symbols which appears more than once under certain approximation types in a data sequence. We define three approximation types, i.e., longer_length, shorter_length, and equal_length. The problems of finding approximate repeating patterns with respect to the three types are specified. By applying the concept of ‘cut’ and ‘pattern_join’ operator, we develop a level-wise approach to solve the problem of finding approximate repeating patterns with respect to the type of longer_length approximation. In addition, we extend the pattern_join operator to the generalized_pattern_join operator for efficiently finding long patterns. The performance study shows that our approach is efficient and also scales well. We also refine our approach to extract repeating patterns from polyphonic music data.
Abstracti
Acknowledgementiii
Contentsiv
List of Figuresvii
List of Tablesxi
1. Introduction1
1.1. Related Work of Musical Information Retrieval3
1.2. Related Work of Finding Patterns from Sequence Data5
2. Various Techniques for Content-based Music Information Retrieval7
2.1. The Representation of Music Objects7
2.1.1) Chord7
2.1.2) Mubol8
2.1.3) Music Segment9
2.1.4) An Extension12
2.2. Indexing13
2.2.1) Suffix Tree-based Indexing13
2.2.2) (N-gram+Tree)-based Indexing14
2.2.3) Augmented Suffix Tree-based Indexing16
2.3. Query Processing18
2.3.1) Exact Matching18
2.3.2) Template-based Matching20
2.3.3) Thresholding-based Matching21
2.4. Discussion25
2.4.1) Comparison of Representation Methods26
2.4.2) Comparison of Indexing and Query Processing27
2.4.3) Intrinsic Difficulties28
3. The Ultima Project31
3.1. System Design, Implementation, and Issues31
3.1.1) Data Set33
3.1.2) Query Set Generation34
3.1.3) Efficiency and Effectiveness Study34
3.2. Description of the Approaches35
3.2.1) The 1D-List Approach35
3.3. The Efficiency Study36
3.3.1) Index Construction36
3.3.2) Exact Query Processing38
3.3.3) Summary of the Experiment Results44
4. Discovering Non-trivial Repeating Patterns47
4.1. Music Features and Repeating Patterns47
4.1.1) Music Features47
4.1.2) Non-trivial Repeating Patterns49
4.2. The Correlative-Matrix Approach50
4.2.1) Approach Overview50
4.2.2) Algorithms for the Correlative-Matrix Approach54
4.3. The String-Join Approach56
4.3.1) Approach Overview56
4.3.2) Algorithms for the String-Join Approach57
4.4. Performance Evaluation and Semantic Analysis63
4.4.1) Experiment Set-up63
4.4.2) Performance Analysis65
4.4.3) Semantics Analysis69
5. Finding Approximate Repeating Patterns73
5.1. Applications73
5.1.1) Application 1: Feature Extraction from Music Data73
5.1.2) Application 2: Accessing Patterns from Web Logs74
5.2. Problem Formulation75
5.2.1) The Definitions75
5.2.2) The Problem78
5.3. Our Approach81
5.3.1) Basic Approach81
5.3.2) Refined Approach86
5.4. Performance Evaluation88
5.4.1) Experiment Set-up89
5.4.2) Performance Evaluation89
5.5. An Extension to Polyphonic Music Objects92
5.5.1) The Definition94
5.5.2) The Problem96
5.5.3) The Approach97
6. Conclusion99
6.1. Future Work100
References:103
Appendix A:109
Appendix B:111
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