Computational Models of Music Similarity and their ... - OFAI

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2.2.7.3 Always Dissimilar . . . . . . . . . . . . . . . . 51 2.3 Optimization and Evaluation . . . . . . . . . . . . . . . . . . . . 51 2.3.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . 53 2.3.1.1 MIREX . . . . . . . . . . . . . . . . . . . . . . 56 2.3.2 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 2.3.4 Artist Filter . . . . . . . . . . . . . . . . . . . . . . . . 64 2.3.5 Optimization . . . . . . . . . . . . . . . . . . . . . . . . 67 2.3.5.1 Combining Two (Feature Selection) . . . . . . . 67 2.3.5.2 Combining Seven . . . . . . . . . . . . . . . . 69 2.3.5.3 Overfitting . . . . . . . . . . . . . . . . . . . . 74 2.3.6 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 76 2.3.6.1 Analyzing the Improvement . . . . . . . . . . . 77 2.3.6.2 Impact of the Length of the Analyzed Audio . . 82 2.3.6.3 Listening Test . . . . . . . . . . . . . . . . . . 82 2.4 Limitations and Outlook . . . . . . . . . . . . . . . . . . . . . . 88 2.5 Alternative: Web-based Similarity . . . . . . . . . . . . . . . . . 89 2.5.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . 89 2.5.2 Similarity Computations (Technique) . . . . . . . . . . . 89 2.5.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . 91 2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 2.6.1 Optimization and Evaluation Procedures . . . . . . . . . 92 2.6.2 Recommendations . . . . . . . . . . . . . . . . . . . . . 92 3 Applications 93 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.2 Islands of Music . . . . . . . . . . . . . . . . . . . . . . . . . . 94 3.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 95 3.2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . 96 3.2.3 The Self-Organizing Map . . . . . . . . . . . . . . . . . 97 3.2.4 Visualizations . . . . . . . . . . . . . . . . . . . . . . . 99 3.2.4.1 Smoothed Data Histograms . . . . . . . . . . . 99 3.2.4.2 Weather Charts and Other Visualizations . . . . 100 3.2.5 Aligned Self-Organizing Maps . . . . . . . . . . . . . . . 102 3.2.5.1 Details . . . . . . . . . . . . . . . . . . . . . . 102 3.2.5.2 Illustration . . . . . . . . . . . . . . . . . . . . 104 3.2.6 Browsing Different Views (Demonstration) . . . . . . . . 105 3.2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 110 3.3 Fuzzy Hierarchical Organization . . . . . . . . . . . . . . . . . . 110 3.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 111
3.3.2 Background . . . . . . . . . . . . . . . . . . . . . . . . 111 3.3.3 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . 116 3.3.4 Term Selection for Cluster Description . . . . . . . . . . 118 3.3.4.1 Techniques . . . . . . . . . . . . . . . . . . . . 119 3.3.4.2 Domain-Specific Dictionary . . . . . . . . . . . 120 3.3.5 Results and Discussion . . . . . . . . . . . . . . . . . . . 121 3.3.5.1 User Interface . . . . . . . . . . . . . . . . . . 122 3.3.5.2 Comparison of Term Selection Techniques . . . 124 3.3.5.3 Discussion . . . . . . . . . . . . . . . . . . . . 124 3.3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 125 3.4 Dynamic Playlist Generation . . . . . . . . . . . . . . . . . . . . 125 3.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 125 3.4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . 126 3.4.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 3.4.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 128 3.4.4.1 Procedure (Hypothetical Use Cases) . . . . . . 129 3.4.4.2 Data . . . . . . . . . . . . . . . . . . . . . . . 129 3.4.4.3 Results . . . . . . . . . . . . . . . . . . . . . . 130 3.4.5 Implementation and Examples . . . . . . . . . . . . . . . 134 3.4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 136 3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 4 Conclusions 139
Page 1: DISSERTATION Computational Models o
Page 5: Abstract This thesis aims at develo
Page 8 and 9: evaluate similarity measures for dr
Page 13 and 14: Chapter 1 Introduction This chapter
Page 15 and 16: 1.1 Outline of this Thesis 3 measur
Page 17 and 18: 1.2 Matlab Syntax 5 ◦ Development
Page 19 and 20: 1.2 Matlab Syntax 7 A frequently us
Page 21 and 22: Chapter 2 Audio-based Similarity Me
Page 23 and 24: 2.1 Introduction 11 Experts High qu
Page 25 and 26: 2.2 Techniques 13 2.2 Techniques To
Page 27 and 28: 2.2 Techniques 15 Amplitude 0 ZCR:
Page 29 and 30: 2.2 Techniques 17 MFCCs Mel Frequen
Page 31 and 32: 2.2 Techniques 19 Segment wav(idx)
Page 33 and 34: 2.2 Techniques 21 Triangular Filter
Page 35 and 36: 2.2 Techniques 23 num_coeffs = 5 nu
Page 37 and 38: 2.2 Techniques 25 2.2.2.5 Parameter
Page 39 and 40: 2.2 Techniques 27 used for clusteri
Page 41 and 42: 2.2 Techniques 29 FFT window size w
Page 43 and 44: 2.2 Techniques 31 Unlike G30 no ran
Page 45 and 46: 2.2 Techniques 33 2.2.3.4 Single Ga
Page 47 and 48: 2.2 Techniques 35 Blue Rondo ... Ka
Page 49 and 50: 2.2 Techniques 37 G30 G30S G1 G1 re
Page 51 and 52: 2.2 Techniques 39 Relative Fluctuat
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Page 55 and 56: 2.2 Techniques 43 2.2.5.1 Time Doma
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2.2 Techniques 49 2.2.6 Linear Comb
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Chapter 3 Applications This chapter
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3.2 Islands of Music 95 Figure 3.1:
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3.2 Islands of Music 97 they use to
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3.2 Islands of Music 99 a b c d Fig
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3.2 Islands of Music 101 AMBIENT CL
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3.2 Islands of Music 103 Figure 3.6
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3.2 Islands of Music 105 scribing a
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3.5 Conclusions 137 + Punk / Bad Re
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Chapter 4 Conclusions In this thesi
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Bibliography [AHH + 03] Eric Allama
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[CKGB02] Pedro Cano, Martin Kaltenb
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[Got03] Masataka Goto, A Chorus-Sec
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[Lüb05] Dominik Lübbers, SoniXplo
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[PFW05b] , Improvements of Audio-Ba
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[SKW05a] Markus Schedl, Peter Knees
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Elias Pampalk I was born in 1978 in
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