Mitchell, T. J. (2010) An exploration of evolutionary computation ...

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7.27: FM oboe simulation, isolated time domain waveform produced by simple FM element 1 (top), element 2 (middle) and element 3 (bottom) ………………………………………………………………………. 7.28: FM oboe simulation, isolated frequency spectrograms produced by simple FM element 1 (top), element 2 (middle) and element 3 (bottom) ……………………………………………………………………..... 7.29: FM oboe simulation, isolated long-term average frequency spectrum produced by simple FM element 1 (top), element 2 (middle) and element 3 (bottom) ……………………………………………………….. 7.30: Long-term average frequency spectrum for triple simple FM oboe simulation (top) and original oboe sound (bottom) …………………………………………………………………………………………… 8.1: Example target sound and five matches with increasing relative spectral error …………………………. 192 8.2: Listening test 1 interface .………………………………………………………………………………… 193 8.3: Listening test 1instruction sheet …………………………………………………………………………. 194 8.4: Tone 3 target (top), match 1(middle) analytically ranked 2 nd perceptually ranked 3 rd , match 2 (bottom) analytically ranked 3 rd perceptually ranked 2 nd …………………………………………………………. 8.5: Listening test 2 interface …………………………………………………………………………………. 197 8.6a: Listening test 2 instruction sheet 1 ………………………………………………………………………. 198 8.6b: Listening test 2 instruction sheet 2 .……………………………………………………………………… 199 8.6c: Listening test 2 instruction sheet 3 ………………………………………………………………………. 200 8.7: Piano target and matched sound time and frequency plots ………………………………………………. 201 8.8: Trumpet target and matched sound time and frequency plots ……………………...……………………. 204 8.9: Violin target and matched sound time and frequency plots ……………………………………………… 206 8.10: Cymbal target and matched sound time and frequency plots ……………………………………………. 207 185 186 187 188 196 x
List of Tables 4.1: MTQ function parameters ………………………………………………………………………………… 72 4.2: Algorithmic variations for convergence comparison ……………………………………………………... 75 4.3: Multiple solution results of experiments on Himmelblau‘s function ……………………………………... 80 4.4: Multiple Solution results of experiments on the multimodal function ……………………………............ 81 4.5: Multiple Solution results of experiments on the waves Function ………………………………………… 83 4.6: Multiple Solution results of experiments on the n-dimensional sine function …………………………..... 85 4.7: Multiple Solution results of experiments on the multimodal function ………………………………….... 87 5.1: Results of CCCES applied to Himmelblau‘s function ……………………………………………………. 107 5.2: Results of CCCES applied to Himmelblau‘s function with increased cluster quantity …………………... 110 5.3: Results of CCCES applied to the multimodal function …………………………………………………... 112 5.4: Results of CCCES applied to the n-dimensional multimodal function …………………………………… 115 5.5: Results of CCES (various) and CCCES applied to the MTQ function …………………………………… 117 5.6: Results of CCCES applied to the MTQ 2 function ………………………………………………………… 118 7.1: Synthesis parameter summary …………………………………………………………………………….. 145 7.2: Problem space dimensionality summary ………………………………………………………………….. 145 7.3: Results when matching Single Simple FM contrived tones ………………………………………………. 154 7.4: Top 10 multiple solutions delivered by the CES for a contrived match ………………………………….. 156 7.5: 10 multiple solutions delivered by the CCCES for a contrived match …………………………………… 156 7.6: Results when matching Double Simple FM contrived tones ……………………………………………... 158 7.7: Results when matching Triple Simple FM contrived tones ………………………………………………. 159 7.8: Top 10 multiple solutions delivered by the CES for a contrived match ………………………………….. 162 7.9: Additive-subtractive target tone specifications …………………………………………………………… 164 7.10: Additive-subtractive target tone results …………………………………………………………………... 166 7.11: Acoustic target fundamental frequencies …………………………………………………………………. 167 7.12: Acoustic target tone matching results …………………………………………………………………….. 168 7.13: Results when matching time-varying single simple FM contrived sounds ……………………………….. 172 7.14: Results when matching time-varying double simple FM contrived sounds ……………………………… 174 7.15: Results when matching time-varying triple simple FM contrived sounds ………………………………... 176 7.16: Acoustic target fundamental frequencies …………………………………………………………………. 179 7.17: Acoustic target time-varying matching results …………………………………………………………… 180 7.18: Oboe matching results …………………………………………………………………………………….. 182 8.1: Listening test one correlation and reliability results ……………………………………………………… 195 8.2: Piano semantic differential results ………………………………………………………………………... 203 8.3: Trumpet semantic differential results ……………………………………………………………………... 205 8.4: Violin semantic differential results ……………………………………………………………………….. 206 8.5: Cymbal semantic differential results …………………………………………………………………….. 208 xi
Page 1 and 2: Mitchell, T. J. (2010) An explorati
Page 3 and 4: Acknowledgments With so many people
Page 5 and 6: Abstract With the ever-increasing c
Page 7 and 8: 4 A Clustering-Based Niching Evolut
Page 9 and 10: 7.7.1.3 Contrived Matching with Tim
Page 11: 5.10: Mean and 95% confidence inter
Page 15 and 16: To experimental musicians lacking t
Page 17 and 18: 1.1.2 Frequency Modulation Audio Sy
Page 19 and 20: The Bessel functions for are shown
Page 21 and 22: 1.3 Contributions In satisfying the
Page 23 and 24: Chapter 2 Background: Evolutionary
Page 25 and 26: Figure 2.1: The evolutionary model
Page 27 and 28: 2.3 Canonical Evolutionary Algorith
Page 29 and 30: it position (locus) may be absent o
Page 31 and 32: t = 0; initialise P μ(t); loop beg
Page 33 and 34: When The benefit of (intermediate)
Page 35 and 36: sphere, or hypersphere, dependent u
Page 37 and 38: the rule may only be applied when a
Page 39 and 40: Firstly, there is no guarantee that
Page 41 and 42: As the research field of evolutiona
Page 43 and 44: five of this thesis are based. The
Page 45 and 46: sparsely throughout the search spac
Page 47 and 48: Selection Pressure - The rate at wh
Page 49 and 50: enable the formation of species, it
Page 51 and 52: 3.4.1.2 Restricted Tournament selec
Page 53 and 54: over the landscape, and the dilutin
Page 55 and 56: Species 1 Genus Figure 3.2: Meta-ES
Page 57 and 58: diffusion model, on the other hand,
Page 59 and 60: fitness Niche Centre Figure 3.4: Ni
Page 61 and 62: also applied within each subpopulat
Page 63 and 64:
outperformed by a simple GA. A seco
Page 65 and 66:
4.1 The Fuzzy Clustering Evolution
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membership to each cluster. In prac
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Fuzzy Intermediate Recombination -
Page 71 and 72:
Parents are subsequently merged and
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4.2.3 New Recombination Operators T
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Global - the ability of the algorit
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period in which all sub-populations
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4.3.2.1 Experiments on the Multimod
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Discussion The canonical ES variant
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Results (20+140) / CES / discrete /
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function. The parameters for the fi
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Discussion The results acquired fro
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these experiments as the quantity o
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Performance Criteria In assessing t
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discrete recombination is employed;
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Figure 4.15: Mean and 95% confidenc
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Discussion These results corroborat
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Figure 4.19: Mean and 95% confidenc
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partitioned into five clusters, the
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However, the question arises as to
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The most consistent finding of Wieg
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Wiegand demonstrated that a pseudo-
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accurate approximation of an interv
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cooperating subpopulation into mult
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5.3.1.1 Collaboration One notable d
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If one representative is selected f
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The CCCES algorithm begins by initi
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For the subsequent experiments, the
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Discussion The results shown in tab
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Figure 5.7: Maximum-fitness curve f
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dimensional multimodal function wit
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From the results shown in Table 5.3
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that, as the dimensionality of the
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only a moderate increase in fitness
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It is interesting that the results
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Niching - it is desirable that mult
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6.2 Synthesiser Choice Since the fo
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Modulating Oscillators Carrier Osci
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6.4 Sound Synthesis Applications of
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with results again presented using
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employed by commercial synthesis ma
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While these metrics are able to suc
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Unlike the metrics considered in se
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Figure 6.6 provides a plot of the l
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6.5 Summary of this Chapter In this
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constant spectral form as static to
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Figure 7.2a represents the most fun
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7.3 Evolutionary Matching Synthesis
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If the frame size is assigned a pow
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an exhaustive search yields no bett
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CCES, in which each dimension of th
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7.6.1.1 Contrived Matching with Sin
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elative spectral error ranged from
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the second, where the parameters fo
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Discussion Figure 7.8: Mean and 95%
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and relative spectrum error is exam
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The flat spectrum produced by the a
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Matching Model Single Simple FM Dou
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Acoustic Target Matching Results Fi
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Figure 7.17: Muted trumpet tone (to
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Performance Criteria As before, res
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indicated the discreet recombinatio
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7.7.1.3 Contrived Matching with Tim
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(a): Target sound time waveform (b)
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Matching Model Single Simple FM Dou
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Algorithmic Parameters Oboe Target
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The frequency spectrograms are plot
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Frequency (Hz) Frequency (Hz) Frequ
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Amplitude (dB) Amplitude (dB) Figur
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Chapter 8 Listening Tests Despite t
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Amplitude (dB) Amplitude (dB) Ampli
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Listening Test One - Similarity Ran
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frequency harmonics well but the mi
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Listening Test Two - General Sound
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Figure 8.6c: Listening test two ins
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The FM sound produced by the matchi
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Frequency (Hz) Amplitude four descr
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Frequency (Hz) Amplitude (a) Violin
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capabilities of the triple simple F
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Chapter 9 Conclusions and Further W
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environments. It was shown that the
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Also included in chapter seven, was
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matching algorithm to probe the und
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The author hopes that this work goe
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Bäck, T., and Schutz, M. (1996) In
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Chowning, J. M. (1973) The synthesi
Page 237 and 238:
Eshelman, L. J. (1990) The CHC Adap
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Hanagandi, V. and Nikolaou, M. (199
Page 241 and 242:
Huband, S., Hingston, P., While L.
Page 243 and 244:
Lim, S. M. and B. T. G. Tan. (1999)
Page 245 and 246:
Miller, B. L., and Goldberg, D. E.
Page 247 and 248:
Popovici, E. and De Jong, K. (2005)
Page 249 and 250:
Rudolph, G (1991) Global Optimizati
Page 251 and 252:
Tan, B. T. G. and S. M. Lim, (1996)
Page 253 and 254:
Wiegand, R. P., and Sarma, J. (2004
Page 255:
Appendix 1 - Listening Test 1 - Res
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