1. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL Website report 2006.10.19 Sungkyunkwan Univ. Aug 22, 2014 Chang Wook Ahn SEALSungkyunkwan Evolutionary Algorithm Lab Automatic Evolutionary Music Composition

2. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 2 Introduction Algorithmic Composition A way of composing music using computational methods Evolutionary Music Composition Evolutionary Algorithm Algorithmic music Evolutionary Art 3. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 3 Introduction Brief History , Algorithmic Composition - Mozart Musical Dice Game - Kirnberger Hydne Random number - Mozart, Bach, Bartok Fibonacci numbers Golden section 1980 machine learning optimization technique computer-aided composition knowledge-based systems, neural networks, genetic algorithms NEUROGEN, GenDash, GenJam, GP-Music System 4. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 4 Categories of EC-based Composition (Automatic System) Evaluate fitness by the musically meaningful criteria , , Zipf , , Often, automatic system faces failure situations False positive: High fitness but musically not good False negative: Low fitness but musically good Consequently, the music doe not possibly reflect the users preference (Interactive System) Evaluate fitness by the users judgment itself Require a lot of time and effort, and difficult to keep consistency in evaluation Evolutionary Music Composition System Human Mentor Population of composition Evaluation Listen Filtering (Artificial Neural Network) 5. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL GA Mapping value of notes and rest (music) : 4(quadruple tune) 8 (quaver) 5 Genetic Representation of Melody Rest Hold B3 C4 C#4 D4 D#4 E4 F4 F#4 G4 G#4 -1 0 59 60 61 62 63 64 65 66 67 68 -1 60 64 71 69 0 0 76 74 71 72 60 62 0 0 0 -1 64 76 74 72 0 0 71 69 67 79 77 76 0 0 0 middle 6. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 6 Genetic Operators (Crossover, Mutation) -1 69 67 70 70 0 -1 0 76 79 76 75 74 72 69 67 76 79 76 75 74 0 -1 0 67 69 67 70 70 72 69 67 Parent 1 Parent 2 Child 1 Child 2 musically meaningful Crossover : melodic interval Mutation : Motif development technique (repetition , Reverse, Transpose ) Parent 1 Parent 1 Child 1 Child 2 7. SEAL. Multiobjective Optimization MO has several conflicting objectives to be maximized simultaneously Due to their interdependence A set of alternative solutions exists The solutions, known as Pareto-optimal set, are optimal in the sense that no solution is superior to them overall as no objective can be improved without degrading the others; where indicates that x1 (Pareto) dominates x0. The image of the Pareto-optimal set is defined as the Pareto optimal solutions Multiobjective Optimization }|)(,),(),({ 000 2 0 1 QfffF n xxxx )()(:)()(: 1010 xxxx jjii ffjffi f1 f2 dominates dominate d indifferen t indifferen t}:|{ 0110 xxxx fAQ 01 xx x1 x0 Pareto optimal Comfort Economy 8. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 8 Fundamentally, music evaluation contains multi-objective aspect = 0 _0 + 1 _1 + 2 _2 (0, 1, 2) , Convex Only one solution is focused w.r.t. the used weights Thus, the concept of Pareto Optimality is used for the fitness evaluation (dominated) 1st Front Pareto Optimality Multi-objective GA Trade-off : Stability and Tension Pareto optimal set () Multi-objective Fitness f2 1st front2nd front 3rd front f1 9. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 9 Fitness Evaluation Multi-objective fitness function Chord Chord tone Non-chord tone(Tension note + Avoid note ) Chord tone , Non-chord tone Fitness 1 Chord ton, Fitness 2 Non-chord tone Fitness = maximize (Fitness 1, Fitness 2) C Key Chord tone Tension note Avoid note C C,E,G D,A,B F Dm D,F,A E,G,C B Em E,G,B A,D F,C FM F,A,C G,B,D,E - G G,B,D A,E,F C Am A,C,E B,D,G F Bmb5 B,D,F# E,G,A C Fitness 1 Fitness 2 1. Chord tone +50 -10 2. Tension note Tension Note -20 +50 At strong beat -50 -30 3. Resolved tension +10 +30 4. Avoid note -30 -5 5. Non-scale note -40 -20 6. Motion Stepwise +10 Stepwise After leap +20 7. Interval Perfect +10 -5 Greater than octave -20 Diatonic chords of the C major key Fitness evaluation parameters 10. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 10 Chord progression Rhythm sequence Initialize population popul ation Copy Genetic operations New popul ation Copy 1 2 3 4 1 2 Reassign rank Stop? Set of melodies Users choice Final composition Rejected Yes No Flowchart of the proposed system Assign rank 11. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 0 50 100 150 200 250 300 350 400 450 500 0 200 400 600 800 1000 11 Two extreme cases Evolution of the Pareto front f1 f2 1 2 3 4 5 6 7 1) f1 600 -20 10 0 0 170 30 790 f2 -120 50 30 0 0 170 -15 115 2) f1 400 -100 50 0 0 170 20 540 f2 -80 250 150 0 0 170 -10 480 Fitness values Experiment 1 4 bar melody composition 1) 2) C F G C C F G C 12. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 12 Two extreme cases Evolution of the Pareto front f1 f2 1 2 3 4 5 6 7 1) f1 550 -40 20 0 0 150 60 740 f2 -110 100 60 0 0 150 -30 170 2) f1 450 -80 40 0 0 190 0 600 f2 -90 200 120 0 0 190 0 420 Fitness values Experiment 2 4 bar melody composition 1) 2) Am F C G 0 50 100 150 200 250 300 350 400 450 0 200 400 600 800 Am F C G 13. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEAL 13 Experiment 3 16 bar melody composition C C F G F C G C Am F C G F C G C C C F G F C G C Am F C G F C G C Two extreme cases Fitness 1: 2760 81% Chord tone Fitness 2: 855 13% Non-chord tone, 6% Rest Fitness 1: 1890 63% Chord tone Fitness 2: 1900 31% Non-chord tone, 6% Rest 14. SEALSungkyunkwan EvolutionaryAlgorithm Lab SEALSEAL SEALSungkyunkwan EvolutionaryAlgorithm Lab 14 Thank you for listening!