Book Image

Mapping and Visualization with SuperCollider

By : Marinos Koutsomichalis
Book Image

Mapping and Visualization with SuperCollider

By: Marinos Koutsomichalis

Overview of this book

SuperCollider is an environment and programming language used by musicians, scientists, and artists who work with audio-files SuperCollider has built-in graphical features which are used in conjunction with the sound synthesis server to create audio-visual mapping and sound visualization. If you wish to create data visualizations by acquiring data from audio and visual sources, then this book is for you.Digital sound artists need to analyze, manipulate, map, and visualize data when working on a scientific or an artistic project. As an artist, this book, by means of its numerous code examples will provide you with the necessary knowledge of SuperCollider's practical applications, so that you can extract meaningful information from audio-files and master its visualization techniques. This book will help you to prototype and implement sophisticated visualizers, sonifiers, and complex mappings of your data.This book takes a closer look at SuperCollider features such as plotting and metering functionality to dispel the mysterious aura surrounding the more advanced mappings and animation strategies. This book also takes you through a number of examples that help you to create intelligent mapping and visualization systems. Throughout the course of the book, you will synthesize and optimize waveforms and spectra for scoping as well as extract information from an audio signal. The later sections of the book focus on advanced topics such as emulating physical forces, designing kinematic structures, and using neural networks to enable you to develop a visualization that has a natural motion with structures that respect anatomy and which come with an intelligent encoding mechanism. This book will teach you everything you need to work with intelligent audio-visual systems to extract and visualize audio-visual data.
Table of Contents (16 chapters)

Machine listening


So far we've examined in detail how we can acquire data from various sources. In a visualization context, however, we may encounter situations wherein we will need to control some elements of an animation with respect to some particular characteristic of a signal, for example, their amplitude or their frequency. Yet, these kinds of information are attributes of the signal, rather than parts of it. In other words, we need something to happen not with respect to some existent data (that is, our signal in this context) but with respect to certain characteristics of a data flow. Consider that an audio signal is completely unaware of how loud it is or of what its frequency is. Remember that audio signals are merely streams of numbers and that sounds are merely fluctuations of air pressure. The reason we understand sounds as having loudness or pitch, is because our auditory apparatus analyzes them and provides the brain with information on certain sonic qualities. Further, more...