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The Continuator: Musical Interaction With Style. Presented by Ching-Hua Chuan ISE 575, Spring 2007, March 29, USC. Introduction. Real-time interactive musical instruments that are able to produce stylistically consistent music.

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the continuator musical interaction with style

The Continuator: Musical Interaction With Style

Presented by Ching-Hua Chuan

ISE 575, Spring 2007, March 29, USC

introduction
Introduction
  • Real-time interactive musical instruments that are able to produce stylistically consistent music.
  • The system learns music styles automatically, and is seamlessly integrated in the playing mode of the musicians.
  • Interactive v.s. imitation systems
inside the continuator
Inside the Continuator
  • Architecture
  • Automatic learning and generation
    • Prefix trees with indexing scheme
    • Variable-order Markov models
    • Reduction functions
    • Polyphony and rhythm
  • Interactive musical instrument
    • Biasing Markov generation
variable order markov models generating the sequence

Input seq. 1: {A B}

  • continuation_list = {3, 7}
  • randomly pick B
Variable-order Markov models(generating the sequence)

Start again with {A B B}

 continuation_list = {8}

Learned prefix trees

  • Start again with {A B B C}
  • no match
  • continuation_list({B C}) = {4}

Learned seq. :{A B C D A B B C }

1 2 3 4 5 6 7 8

Ending seq = {A B B C D}

reduction functions

Less refined reduction function. (pitch region instead of pitch)

- learned: {PR1 PR1 PR2 PR3 PR5} - Input: {PR1 PR1 PR2}

- Continuation = {PR3}, which is G in this case.

  • Hierarchy of reductions

- pitch * duration * velocity

- small pitch region * velocity

- small pitch regions

- large pitch regions

Cont’d

Reduction Functions
  • No continuation is found.

learned

Input

polyphony and rhythm
Polyphony and Rhythm
  • Polyphony: legato v.s. musical cluster
  • Rhythm (jazz and popular music)

- Natural, linear, input rhythms

- Fixed metrical structure

chord

legato

Fixed metrical structure

biasing markov generation
Biasing Markov Generation
  • Weighting the nodes according to how they match the external input.

S=1, we get a musical automaton insensitive to the musical context,

S=0, we get a reactive system which generates the closest musical

elements to the external input it finds in the database.

experiments
Experiments

Bernard Lubat

The Continuator and children

Gyorgy Kurtag

Claude Barthelemy

Video from http://www.csl.sony.fr/Research/Experiments/Continuator/index.php

slide11
Demo
  • Let’s play with it!