Full metadata
Title
Timbral learning for musical robots
Description
The tradition of building musical robots and automata is thousands of years old. Despite this rich history, even today musical robots do not play with as much nuance and subtlety as human musicians. In particular, most instruments allow the player to manipulate timbre while playing; if a violinist is told to sustain an E, they will select which string to play it on, how much bow pressure and velocity to use, whether to use the entire bow or only the portion near the tip or the frog, how close to the bridge or fingerboard to contact the string, whether or not to use a mute, and so forth. Each one of these choices affects the resulting timbre, and navigating this timbre space is part of the art of playing the instrument. Nonetheless, this type of timbral nuance has been largely ignored in the design of musical robots. Therefore, this dissertation introduces a suite of techniques that deal with timbral nuance in musical robots. Chapter 1 provides the motivating ideas and introduces Kiki, a robot designed by the author to explore timbral nuance. Chapter 2 provides a long history of musical robots, establishing the under-researched nature of timbral nuance. Chapter 3 is a comprehensive treatment of dynamic timbre production in percussion robots and, using Kiki as a case-study, provides a variety of techniques for designing striking mechanisms that produce a range of timbres similar to those produced by human players. Chapter 4 introduces a machine-learning algorithm for recognizing timbres, so that a robot can transcribe timbres played by a human during live performance. Chapter 5 introduces a technique that allows a robot to learn how to produce isolated instances of particular timbres by listening to a human play an examples of those timbres. The 6th and final chapter introduces a method that allows a robot to learn the musical context of different timbres; this is done in realtime during interactive improvisation between a human and robot, wherein the robot builds a statistical model of which timbres the human plays in which contexts, and uses this to inform its own playing.
Date Created
2016
Contributors
- Krzyzaniak, Michael Joseph (Author)
- Coleman, Grisha (Thesis advisor)
- Turaga, Pavan (Committee member)
- Artemiadis, Panagiotis (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
xvii, 150 pages : illustrations (some color)
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.40739
Statement of Responsibility
by Michael Joseph Krzyzaniak
Description Source
Viewed on January 5, 2017
Level of coding
full
Note
thesis
Partial requirement for: Ph.D., Arizona State University, 2016
bibliography
Includes bibliographical references (pages 134-143)
Field of study: Media arts and sciences
System Created
- 2016-12-01 07:02:20
System Modified
- 2021-08-30 01:20:40
- 3 years 2 months ago
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