Abstract
This paper describes a work in progress on co-evolving Artificial Neural Networks (ANNs) for music improvisation. Using this neuro-evolutionary approach the ANNs adapt to the changes in the human player's music as input, while still maintaining some of the structure of the musical piece previously evolved. The system is called PRIMAL-IMPROV and evolves modules that are composed of two ANNs, one controlling pitch and one controlling rhythm. The results of a quantitative study show that, by only introducing simple rules as fitness functions, the system is able to generate more interesting arrangements than ANNs evolved without a specific objective. The emerging and interesting musical patterns that are produced by the evolved ANNs hint at the promising potential of the system.
| Original language | English |
|---|---|
| Title of host publication | 2017 9th Computer Science and Electronic Engineering (CEEC) |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 1 Sept 2017 |
| Pages | 172-177 |
| ISBN (Print) | 978-1-5386-3007-5 |
| DOIs | |
| Publication status | Published - 1 Sept 2017 |
Keywords
- evolutionary computation
- music
- neural nets
- PRIMAL-IMPROV
- artificial neural networks
- co-evolutionary musical improvisation
- evolved ANN hint
- human player
- interesting musical patterns
- music improvisation
- neuro-evolutionary approach
- Evolutionary computation
- Network topology
- Neural networks
- Real-time systems
- Rhythm
- Topology