Computer Generated Music Amy Hoover COT 4810 041908

Computer Generated Music Amy Hoover COT 4810 04/19/08

Introduction • Computer-generated music sounds artificial • Current systems battle between: – Knowledge-based approaches • Prescreen song for goodness based on “known musical rules” • Sound trite, uninspired, unoriginal – Other approaches • Allow for more novelty • Sound messy, uncollected

Outline • Representing music • Deciding what is “good” • Problems

Representation • Representation: What is the best way to encode music? – How do we as programmers design a structure to represent music – Intuitive answer: Should music be represented by musical rules and encodings? – Less obvious • Functional relationships through Compositional Pattern Producing Networks (CPPNS)a

Representation: Mathematical Models • Probabilities – Idea: Actual musical notes distributed with certain probability, model with computer program – Without computer: Earliest music generation form (Mozart, Xenakis, Schoenberg) – With computer: Iliac Suite by Hiller and Isaacson (1947)

Representation: Mathematical Models • Probabilities – Idea: Actual musical notes distributed with certain probability, model with computer program Probabilities: A =. 60 B =. 40

Representation: Mathematical Models • Probabilities – Idea: Actual musical notes distributed with certain probability, model with computer program Probabilities: A =. 60 B =. 40 A B A A B

Representation: Markov Chains • Markov chain: “Conditional probability systems where the probability of future events depends on one or more past events” • Probability chart/state transition matrix • E. g. C D E F C 0. 2 0. 1 0. 3 0. 4 D 0. 5 0. 1 0. 2 E 0. 5 0. 2 0. 1 0. 2 F 0. 2 0. 3 0. 2

Representation: Markov Chains • Probability of A following B (P(A|B)) = 0 • Probability of B following A (P(B|A)) =. 5 A B B

Representation: Grammars • Idea: Music and language share similar origin – Relates sentence composition to music composition – Musical -> linguistic relations • Notes -> words • Phrases -> sentences • Melodies ->paragraphs

Representation: Grammars • E. g. – Grammar description – – In: The interval between two notes (5 th, 4 th, etc) Dn: Direction of interval SEQn: Sequence SIMn: Simultaneity • Example generative rule for this grammar – SIM 1 -> SEQ 1 +SEQ 2 – SEQ 1 -> (I 5, D 1) + (I 8, D 1) + (I 11, D 1) – SEQ 2 -> (I 5, D 2) + (I 8, D 2)

Representation: Grammars SIM 1 SEQ 2 (I 5, D 1) (I 8, D 1) (I 11, D 1) (I 5, D 2) (I 8, D 2)

Representation: Neural Networks • Idea: Artificial neural networks abstractions of human brain, can “learn” music by example • Outputs – Pitch, timbre, duration • Network structure: – Recurrent – Compositional Pattern Producing Networks (CPPNs)

Representation: Neural Networks • E. g. – Network encodes note A on the Piano. 21 Pitch node -> Pitch Map Example Timbre Map Example A <=. 33 Bass <=. 33<B<=. 67. 33<Guitar<=. 67<C<=1 . 67<Piano<=1 . 89 <- Timbre map

Determining Goodness: Critic • Idea: Generated music sounds good, neutral, or bad • Critic: Agent to distinguish good and bad – Human – Rule-based – Learning – Evolved

Critic Types: Rule Based • Idea: Music mutated by good, musically sound rules • Given set of melodies, mutate according to musical rules such as – transposition, retrograde, inversion, augmentation • Typically brittle • Confined to style • No room for composers to break the rules – (Even dissonant chords can be resolved!)

Critic Types: Human • Individual/Group • Preserve novelty • Interactive Evolutionary Computation (IEC) – User presented with population – User chooses good individuals – Good individuals parent next generation

Critic Types: Example • Gen. Jam – Generates jazz solos to “trade fours” with Biles – Jazz solo genomes are human evaluated as good or bad by a person – Winning genome accompanies Biles in real time

Critic Types: Human • Problems and Concerns – – Truly creative? User unreliability User fatigue/ Human bottleneck Many-to-one, obfuscates style

Critic Types: Learning-Based • Critics trained with “good” music • Learns to make “good” compositional decisions • Benefits: – A priori knowledge not necessary – Avoids human bottleneck • Disadvantages – Often over trains, does not generalize well

Open Problems in Evolutionary Music • Problem 1: – Current system designs are not recognized for artistic contributions • Problem 2: – Theories behind music/art systems are weak or non-existant

Conclusion • Computer generated music: Sounds artificial • Representation systems – – Probability Markov Chains Formal grammars Neural Networks • Critic types – Human – Ruled-based

Questions • 1. Name two musical representation designs • 2. What kind of critic does Gen. Jam use?

References • Bently, Peter J. , David W. Crone. Creative Evolutionary Systems. Academic Press, 2002. • Miranda, Eduardo Reck. Composing Music with Computers. Focal Press, 2001. • Miranda, Eduardo Reck, Al Biles. Evolutionary Computer Music. Springer-Verlag London, 2007. • Todd, Peter M. , Gareth Loy. Music and Connectionism. MIT Press, 1991. • Todd, Peter M. and Gregory M. Werner. Frankensteinian Methods for Evolutionary Music Composition. MIT Press, 1998.