1. Music Analysis and Generation Supervisors: Jon McCormack & Lloyd Allison Interim Presentation Oliver Ng (B.DigSys)

2. Overview Synthesis of sound -> physical qualities Synthesis of sound -> physical qualities Synthesis of music -> ordering of notes/chords Synthesis of music -> ordering of notes/chords MIDI standard, relevance to this research MIDI standard, relevance to this research Different styles or ‘genres’ of music Different styles or ‘genres’ of music

3. Aims of research Analysis: Finding a set of rules that uniquely identify a genre of music Analysis: Finding a set of rules that uniquely identify a genre of music Generation: Creating similar music in the same genre based on rules Generation: Creating similar music in the same genre based on rules Combination of above two processes Combination of above two processes

4. Basics Note Note Melody Melody Chord Chord

5. Basics “12 tone theory” – 12 semitones/octave “12 tone theory” – 12 semitones/octave Chords, melodies based on subsets of these 12 notes Chords, melodies based on subsets of these 12 notes

6. Basics “12 tone theory” – 12 semitones/octave “12 tone theory” – 12 semitones/octave Chords, melodies based on subsets of these 12 notes Chords, melodies based on subsets of these 12 notes

7. Basics “12 tone theory” – 12 semitones/octave “12 tone theory” – 12 semitones/octave Chords, melodies based on subsets of these 12 notes Chords, melodies based on subsets of these 12 notes

8. Basics “12 tone theory” – 12 semitones/octave “12 tone theory” – 12 semitones/octave Chords, melodies based on subsets of these 12 notes Chords, melodies based on subsets of these 12 notes

9. Analysis Combination of music theory and statistics Combination of music theory and statistics –occurrences of each note –transitions between notes –Base 12 (12 distinct pitches in an octave) Choice of data structures to assist in generation Choice of data structures to assist in generation –Hierarchy (multi-level model)

10. Analysis (continued) Case study of a number of existing genres: Case study of a number of existing genres: –Bluegrass –Modern pop Similarities, differences Similarities, differences Stochastic processes (probability matrices) Stochastic processes (probability matrices)

11. Analysis (continued) Order N Markov Models Order N Markov Models –transition matrices Grammar or rule-based approach Grammar or rule-based approach Genetic, string-matching algorithms: Genetic, string-matching algorithms: –repetitions, variations, mutations.

12. Analysis (continued) Example:

13. Example:

14. Generation Means of testing results, feedback Means of testing results, feedback Based on methods of analysis Based on methods of analysis

15. Expected Results Set of rules defining an arbitrary genre Set of rules defining an arbitrary genre Ability to read in existing MIDI files and extract information based on rules. Ability to read in existing MIDI files and extract information based on rules. Produce musical output of similar quality in MIDI format Produce musical output of similar quality in MIDI format

16. Facilities/requirements Software to analyse/produce MIDI: Software to analyse/produce MIDI: –Haskell Glasgow-Haskell Compiler (GHC) Glasgow-Haskell Compiler (GHC) Haskore modules Haskore modules –C, C++

17. Work in progress (1) Existing MIDI file to be analysed. (2) MIDI music generated based on information extracted from (1).

18. Further information Project website: Project website: http://www.csse.monash.edu.au/~ong Email: Email: ong@csse.monash.edu.au