Generative Arts and Musical Machines
Module code: W3093
30 credits in autumn semester
Teaching method: Seminar, Tutorial *
Assessment modes: Coursework
This module explores the creative application of generative systems in software and hardware, culminating in the creation of a robot orchestra.
Through exploring key topics of generative art (stochastic processes, rule based systems, automata, intelligent systems, physical computing, hacking, circuit bending, data art and robotic art), you will gain the necessary skills required to build your own physical or audiovisual system that contributes to the robot orchestra.
The module will base the practical work on a historical study of artistic and creative use of systems, processes and algorithms in musical composition and generative arts. Historical practices and techniques will be introduced in a mix of lectures and hands-on workshop sessions.
You will also learn more advanced electronics and hardware-building using microcontrollers, such as Arduino, including the use of motors, and the Arduino audio processing audio library, Mozzie. The module's assessment is individual, but you will be working with a group of fellow students from week 4 in building a robotic band, of which your piece is a component.
Prerequisite: CMT or equivalent experience and knowledge of Max/MSP or Pure Data.
Module learning outcomes
- Develop practical skills in physical computing, generative methods and data/ sensor mapping necessary for creative exploration.
- Show historical and aesthetic understanding of the field of generative arts, know its main players, institutions and subsections.
- Demonstrate a working knowledge of the key principles of generative and interactive art through the design and development of a creative component of a robotic ensemble.
- Apply focused design methodologies to choose appropriate algorithms, sensors, and motors, in designing a realistic autonomous robotic module.
- Demonstrate skills in individual and collaborative research, group presentations, debate and peer group critique.