World music technology : culturally sensitive strategies for automatic music prediction

Music has been shown to form an essential part of the human experience-every known society engages in music. However, as universal as it may be, music has evolved into a variety of genres, peculiar to particular cultures. In fact people acquire musical skill, understanding, and appreciation specific to the music they have been exposed to. This process of enculturation builds mental structures that form the cognitive basis for musical expectation. In this thesis I argue that in order for machines to perform musical tasks like humans do, in particular to predict music, they need to be subjected to a similar enculturation process by design. This work is grounded in an information theoretic framework that takes cultural context into account. I introduce a measure of musical entropy to analyze the predictability of musical events as a function of prior musical exposure. Then I discuss computational models for music representation that are informed by genre-specific containers for musical elements like notes. Finally I propose a software framework for automatic music prediction. The system extracts a lexicon of melodic, or timbral, and rhythmic primitives from audio, and generates a hierarchical grammar to represent the structure of a particular musical form. To improve prediction accuracy, context can be switched with cultural plug-ins that are designed for specific musical instruments and genres. In listening experiments involving music synthesis a culture-specific design fares significantly better than a culture-agnostic one. Hence my findings support the importance of computational enculturation for automatic music prediction. Furthermore I suggest that in order to sustain and cultivate the diversity of musical traditions around the world it is indispensable that we design culturally sensitive music technology.

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Sarkar, Mihir, Ph. D. Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Barry L. Vercoe.

• Architecture. Program in Media Arts and Sciences.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/77812

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/77812