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Description
This thesis aims to explore the language of different bodies in the field of dance by analyzing
the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,
the term habitual patterns is defined as the postures or poses that tend to re-appear,
often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing
the movement vocabulary of a dancer to reveal his/her unique fingerprint.
The proposed approach for uncovering these movement patterns is to use a clustering
technique; mainly k-means. In addition to a static method of analysis, this paper uses
an online method of clustering using a streaming variant of k-means that integrates into
the flow of components that can be used in a real-time interactive dance performance. The
computational system is trained by the dancer to discover identifying patterns and therefore
it enables a feedback loop resulting in a rich exchange between dancer and machine. This
can help break a dancer’s tendency to create similar postures, explore larger kinespheric
space and invent movement beyond their current capabilities.
This paper describes a project that distinguishes itself in that it uses a custom database
that is curated for the purpose of highlighting the similarities and differences between various
movement forms. It puts particular emphasis on the process of choosing source movement
qualitatively, before the technological capture process begins.
the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,
the term habitual patterns is defined as the postures or poses that tend to re-appear,
often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing
the movement vocabulary of a dancer to reveal his/her unique fingerprint.
The proposed approach for uncovering these movement patterns is to use a clustering
technique; mainly k-means. In addition to a static method of analysis, this paper uses
an online method of clustering using a streaming variant of k-means that integrates into
the flow of components that can be used in a real-time interactive dance performance. The
computational system is trained by the dancer to discover identifying patterns and therefore
it enables a feedback loop resulting in a rich exchange between dancer and machine. This
can help break a dancer’s tendency to create similar postures, explore larger kinespheric
space and invent movement beyond their current capabilities.
This paper describes a project that distinguishes itself in that it uses a custom database
that is curated for the purpose of highlighting the similarities and differences between various
movement forms. It puts particular emphasis on the process of choosing source movement
qualitatively, before the technological capture process begins.
ContributorsIyengar, Varsha (Author) / Xin Wei, Sha (Thesis advisor) / Turaga, Pavan (Committee member) / Coleman, Grisha (Committee member) / Arizona State University (Publisher)
Created2016
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.
ContributorsKrzyzaniak, Michael Joseph (Author) / Coleman, Grisha (Thesis advisor) / Turaga, Pavan (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2016