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

More documents

Recommendations

Info

30 2 Audio-based Similarity Measures Blue Rondo ... Kathy’s Waltz Bad Med. Bring Me To ... Someday Bolero 20 0 −20 20 0 −20 20 0 −20 20 0 −20 20 0 −20 20 0 −20 10 20 30 10 20 30 10 20 30 10 20 30 10 20 30 10 20 30 Figure 2.10: Illustration of the cluster model for G30. Each column shows different representations of one piece of music. The pieces are the same as the ones used in Figure 2.2. Each plot has the dimensions Mel frequency band on the x-axis and dB on the y-axis. That is, all MFCCs are reconstructed and visualized in the Mel space. The first row is a 2-dimensional histogram of all spectra (MFCCs). The second row are the 30 GMM centers. The gray shading corresponds to the prior of each component (black being the highest prior). The third row shows a flattened probability density of the GMM. In particular, the variance of the components are visible. The fourth row shows the probability of the original MFCC frames. The frame with the highest probability is plotted in black. The fifth row shows the probabilities of the 2000 sample frames that are drawn from the GMM. Most of them are not visible because their probability is very small compared to the highest one. The last row shows the histogram of the sample drawn.
2.2 Techniques 31 Unlike G30 no random samples are drawn from the cluster models. Instead the cluster centers are used as sample (as suggested in [LS01]). However, instead of using the Kullback-Leibler Divergence in combination with the Earth Mover’s Distance, the probability for each point of this sample is computed by interpreting the cluster model as a GMM with diagonal covariances. Since such a sample does not reflect the probability distribution (due to the different priors) the log-likelihood of each sample is weighted according to its prior before summarization: ∑M A L(X A |Θ B ) = Pm A log m=1 ( MB ) ∑ Pm B ′N (µA m|µ B m ′, ΣB m ′) , (2.17) m ′ =1 where M A is the number of centers in model Θ A . P A m is the prior probability of center m of model A. To compute the distances Equation 2.16 is used. L(X A |Θ A ) can be precomputed as no new sample is drawn for each distance computation (unlike G30). MIREX 2005 Genre Classification and Artist Identification A nearest neighbor classifier based on a similarity measure which combined G30S with additional features was submitted to the MIREX 2005 artist identification and genre classification competitions [Pam05]. 13 The main difference to the approach described here is that a FFT window length and hop size of 1024 samples was used. G30S was combined with Fluctuation Patterns and two simple descriptors (Focus and Gravitiy) which will be described later on. In terms of computation time G30S performed very well. However, in terms of the measured accuracy G30S was clearly outperformed by some of the other submissions. These results will be discussed in more detail in Section 2.3. Illustrations Figure 2.11 shows the same plots for G30S which were already discussed for G30 in Figure 2.10. The main observation is that G30 and G30S are highly similar. The only noticeable deviations are in rows 4 and 5. However, these rows significantly deviate if a different random sample is drawn or if the GMM is trained with a different initialization. 13 http://www.music-ir.org/mirex2005
Page 1: DISSERTATION Computational Models o
Page 5: Abstract This thesis aims at develo
Page 8 and 9: evaluate similarity measures for dr
Page 10 and 11: 2.2.7.3 Always Dissimilar . . . . .
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: 2.2 Techniques 29 FFT window size w
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
Page 53 and 54: 2.2 Techniques 41 36 mel 71 1 12 me
Page 55 and 56: 2.2 Techniques 43 2.2.5.1 Time Doma
Page 57 and 58: 2.2 Techniques 45 Alternatively, th
Page 59 and 60: 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 93 and 94:
2.3 Optimization and Evaluation 81
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
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
Page 111 and 112:
3.2 Islands of Music 99 a b c d Fig
Page 113 and 114:
3.2 Islands of Music 101 AMBIENT CL
Page 115 and 116:
3.2 Islands of Music 103 Figure 3.6
Page 117 and 118:
3.2 Islands of Music 105 scribing a
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
3.3 Fuzzy Hierarchical Organization
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
3.4 Dynamic Playlist Generation 125
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
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
Page 163 and 164:
[SKW05a] Markus Schedl, Peter Knees
Page 165:
Elias Pampalk I was born in 1978 in
show all

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

Create successful ePaper yourself

Delete template?

Save as template?