Abstract
Supervised learning methods have long been used to allow musical interface designers to generate new mappings by example. We propose a method for harnessing machine learning algorithms within a radically interactive paradigm, in which the designer may repeatedly generate examples, train a learner, evaluate outcomes, and modify parameters in real-time within a single software environment. We describe our meta-instrument, the Wekinator, which allows a user to engage in on-the-fly learning using arbitrary control modalities and sound synthesis environments. We provide details regarding the system implementation and discuss our experiences using the Wekinator for experimentation and performance.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 280-285 |
| Number of pages | 6 |
| Journal | Proceedings of the International Conference on New Interfaces for Musical Expression |
| State | Published - 2009 |
| Event | 9th International conference on New Interfaces for Musical Expression, NIME 2009 - Pittsburgh, United States Duration: Jun 4 2009 → Jun 6 2009 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Instrumentation
- Music
- Human-Computer Interaction
- Hardware and Architecture
- Computer Science Applications
Keywords
- Machine learning
- Mapping
- Tools