MA Computational Arts

This module brings attention to the relations of computational arts practices. How do artists working with computation undertake research projects? How might computational art develop new theories of computation? What might computational art approaches (scraping, visualising, mapping, sonifying, mining etc.) bring to theoretical practices?

In this module, we consider theory at the intersection of computation and art; and we critique and generate material-discourses of computational art theory. Together we will explore the socio-material implications of computational arts practices, through examples and experiments, to generate alternative theories of computation.

The course will introduce you to a range of techniques and practices for creating interactive audiovisual software using generative techniques. This will include computational and process-based thinking, perspectives on audio and visual perception, algorithms for creating graphics and sound, advanced topics in computational media and project development. Every week a theme from art is introduced and then replicated and examined using code.

Objectives

• Provide the student with a fundamental understanding of code and modern computer literacy.
• Introduce and apply programming concepts and techniques using openFrameworks/C++.
• Approach programming from an artistic perspective.
• Allow for the emergence of open dialogue regarding the content being instructed.
• Invoke the student's interests to apply what they have learned outside of class.

On successful completion you will be able to:

•  Program interactive installations
•  Develop algorithms for generating images and sound
•  Reason about the aesthetics of computer art pieces 

Sample Syllabus:

• Introduction to art, tech, free software
• Introduction to openFrameworks
• Animation and intro to generative art
• Repeat, repeat, repeat: loops and arrays
• Number generators
• Images & video
• Algorithmic thiking
• Sound with maximilian
• Revision and guest artist
•  ----- project work -----

This module builds on Workshops in Creative Coding 1 by assuming that students have mastered the basics of C++ in introduces them to topics in computational arts such as: computer vision, machine learning / AI, networking, genetic algorithms, 3D graphics, emergence of complexity and more.

 Sample Syllabus:

• Emergence and object oriented programming
• Computer vision A (part 1)
• Computer vision B (part 2)
• Networked art with OSC messages
• Machine learning / AI
• Sound with maximilian (part 2)
• Projection mapping
• Genetic algorithms and other advanced generative techniques
• 3D graphics
• ----- project work ----- 

Other topics include:

• Data visualization
• Physics engines
• Mobile
• Shaders / GLSL
• Augmented reality
• DMX
• Delaunay / voronoi
• Art with typography
• Swarm intelligence
• Using 3rd party APIs

Physical Computing is of increasing interest to artists, musicians, choreographers and other creative practitioners for the creation of novel artworks and also for forms of computational interaction between these objects and people. There are many other applications of Physical Computing, for example in museums, ubiquitous and embedded computing, robotics, engineering control systems and Human Computer Interaction.

A physical environment may be sonic, tangible, tactile, visually dynamic, olfactory or any combination of these. In this module, you will learn how the environment, which is essentially continuous, can be monitored by analogue electrical and mechanical sensors. Computers, however, are digital machines programmed by software. One element which you will focus on, therefore, is the interface between the digital and the analogue.

This study will encompass basic physics, electronics, programming and software engineering. The practical objective of this module is the development of the skills you will need for designing and building interactive physical devices.

Students produce an independent research-led practice project in an area of their choosing. They will apply the skills learnt on their MA to a single, coherent body or work. Students will work independently on an area of their choosing under the guidance of a supervisor chosen from the academic staff of the department. Their final peiece goes on display in the degree show exhibition in early September. 

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This course extends the principles of creative engineering for use in arts, games, and more general interaction scenarios so that students can develop their own projects through the use of computational approaches to audiovisual processing. The lessons will be taught using Javascript or C++. It is recommended that students have some experience with using Processing and some background in digital audio and/or digital image manipulation before taking this course. We will spend the first few sessions exploring Digital Audio Signal Processing. This will cover synthesis, sequencing, filtering, sample loading and playback, panning and rudimentary analysis. Following this we’ll be looking at audiovisual interaction using video and 3D graphics.

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This module will expose students to state-of-the-art techniques, tools, and open questions related to creative uses of data, signal processing, and machine learning. The emphasis will be on developing hands-on skills using these techniques in creative projects, and on exploring the creative potential of these techniques.

Specifically, students will learn about topics including:

• Representations and feature engineering for sensor data, audio data, image and video data, social media data, etc.
• Signal processing techniques for working effectively with this data (e.g., perceptual audio and video features, smoothing filters,
  onset detection)
• Communication protocols for working with real-time data (e.g., OpenSoundControl, Web Sockets, serial)
• Applications of classification to creative and interactive contexts: e.g., human pose recognition, activity recognition, semantic
  audio analysis
• Applications of regression to creative and interactive contexts: e.g., creating continuous gestural controllers and multimodal
  mappings (such as music visualisations, gesturally-controlled instruments)
• Applications of temporal modeling to creative and interactive contexts: e.g., gesture recognition, temporal analysis of music or
  video
• Current topics in signal processing and machine learning in music, art, and other creative industries (e.g., Google's "Deep
  Dream," chat bots, image style transfer)
• Tools for working with data, signal processing, and machine learning in creative projects, including tools for real-time data
  analysis
• Reasoning about fundamental questions in machine learning and data mining, including e.g., how can an algorithm learn from
  data? What feature representations should we use for a given problem? How do we know whether one algorithm is better than
  another?

This module gives an understanding of how game design mechanics, dynamics and aesthetics function within playable experiences.

By exploring how play fits into the larger field of possible gameful designs, students will gain conceptual fluency of concepts emerging in this evolving discipline. Using a playcentric approach to design, students will get hands-on experience creating and iterating experiences with players.

Using basic microcontrollers which emulate keyboard controls, students will fabricate basic interfaces for interactivity. This module explores tactics for interweaving mechanics into interactions, aesthetics into design choices and dynamics into successful HCI strategies.

This module is designed to offer advanced material for students who want to specialise in the area of applications in 3D environments and animation. It is geared towards research-led teaching, which would expose students to the most state-of-the-art 3D VR applications.

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