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

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100 3 Applications Figure 3.3: Weather chart metaphor for the distribution of bass energy on the map. the map, which is then visualized using a specific color scale. A Matlab toolbox for the SDH is available online. 1 For details of the impact of the parameter n and a comparison to other cluster visualization techniques see [PRM02b]. Figure 3.2 demonstrates the SDH visualization using a toy example. 3.2.4.2 Weather Charts and Other Visualizations Any attribute (such as an automatically extracted feature describing the bass energy of a piece, or genre metadata) can be used to describe different regions on the map. For example, landmarks such as mountains and hills can be labeled with descriptions which indicate what type of music can be found in the respective area. Details on the labeling of the Islands of Music can be found in [Pam01]. Alternatively a metaphor of weather charts can be used. For example, areas with a strong bass are visualized as areas with high temperatures, while areas with low bass correspond to cooler regions. Hence, for example, it becomes apparent that the pieces are organized so that those with a strong bass are in the west and those with less bass in the east. Figure 3.3 shows an example for a weather chart visualization using the same map shown in Figure 3.1. The contours of the islands are drawn to help the users keep their orientation. Visualizing genre metadata can be very useful to evaluate the organization of a map. An example where a map computed for DB-ML (see Subsection 2.3.3 for a description of this collection) is visualized in terms of the genre distributions is shown in Figure 3.4. Noticeable are that some genres form very compact clusters such as punk. Others such as world are spread over the entire map. 1 www.ofai.at/˜elias.pampalk/sdh
3.2 Islands of Music 101 AMBIENT CLASSICAL ELECTRONIC FOLK JAZZ METAL NEW AGE POP PUNK ROCK WORLD Figure 3.4: Distribution of genres on the SOM computed for DB-ML.
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DISSERTATION Computational Models o
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Abstract This thesis aims at develo
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evaluate similarity measures for dr
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2.2.7.3 Always Dissimilar . . . . .
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Chapter 1 Introduction This chapter
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1.1 Outline of this Thesis 3 measur
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1.2 Matlab Syntax 5 ◦ Development
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1.2 Matlab Syntax 7 A frequently us
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Chapter 2 Audio-based Similarity Me
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2.1 Introduction 11 Experts High qu
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2.2 Techniques 13 2.2 Techniques To
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2.2 Techniques 15 Amplitude 0 ZCR:
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2.2 Techniques 17 MFCCs Mel Frequen
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2.2 Techniques 19 Segment wav(idx)
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2.2 Techniques 21 Triangular Filter
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2.2 Techniques 23 num_coeffs = 5 nu
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2.2 Techniques 25 2.2.2.5 Parameter
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2.2 Techniques 27 used for clusteri
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2.2 Techniques 29 FFT window size w
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2.2 Techniques 31 Unlike G30 no ran
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2.2 Techniques 33 2.2.3.4 Single Ga
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2.2 Techniques 35 Blue Rondo ... Ka
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2.2 Techniques 37 G30 G30S G1 G1 re
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2.2 Techniques 39 Relative Fluctuat
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2.2 Techniques 41 36 mel 71 1 12 me
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2.2 Techniques 43 2.2.5.1 Time Doma
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2.2 Techniques 45 Alternatively, th
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2.2 Techniques 47 ZCR (×10 −3 )
Page 61 and 62: 2.2 Techniques 49 2.2.6 Linear Comb
Page 63 and 64: 2.3 Optimization and Evaluation 51
Page 101 and 102: 2.5 Alternative: Web-based Similari
Page 103 and 104: 2.6 Conclusions 91 2.5.3 Limitation
Page 105 and 106: Chapter 3 Applications This chapter
Page 107 and 108: 3.2 Islands of Music 95 Figure 3.1:
Page 109 and 110: 3.2 Islands of Music 97 they use to
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Page 117 and 118: 3.2 Islands of Music 105 scribing a
Page 123 and 124: 3.3 Fuzzy Hierarchical Organization
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Page 149 and 150: 3.5 Conclusions 137 + Punk / Bad Re
Page 151 and 152: Chapter 4 Conclusions In this thesi
Page 153 and 154: Bibliography [AHH + 03] Eric Allama
Page 155 and 156: [CKGB02] Pedro Cano, Martin Kaltenb
Page 157 and 158: [Got03] Masataka Goto, A Chorus-Sec
Page 159 and 160: [Lüb05] Dominik Lübbers, SoniXplo
Page 161 and 162: [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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