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Machine Learning for Inspired, Struc...

Bodily, Paul Mark.

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Machine Learning for Inspired, Structured, Lyrical Music Composition.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning for Inspired, Structured, Lyrical Music Composition./
Author:	Bodily, Paul Mark.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	152 p.
Notes:	Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Contained By:	Dissertation Abstracts International80-02B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10845187
ISBN:	9780438458017

Machine Learning for Inspired, Structured, Lyrical Music Composition.
Bodily, Paul Mark.

Machine Learning for Inspired, Structured, Lyrical Music Composition. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 152 p.

Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.

Thesis (Ph.D.)--Brigham Young University, 2018.

Computational creativity has been called the "final frontier" of artificial intelligence due to the difficulty inherent in defining and implementing creativity in computational systems. Despite this difficulty computer creativity is becoming a more significant part of our everyday lives, in particular music. This is observed in the prevalence of music recommendation systems, co-creational music software packages, smart playlists, and procedurally-generated video games. Significant progress can be seen in the advances in industrial applications such as Spotify, Pandora, Apple Music, etc., but several problems persist. Of more general interest, however, is the question of whether or not computers can exhibit autonomous creativity in music composition. One of the primary challenges in this endeavor is enabling computational systems to create music that exhibits global structure, that can learn structure from data, and which can effectively incorporate autonomy and intention.

ISBN: 9780438458017Subjects--Topical Terms:

523869
Computer science.

Machine Learning for Inspired, Structured, Lyrical Music Composition.
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300 $a 152 p.
500 $a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
500 $a Adviser: Dan Ventura.
502 $a Thesis (Ph.D.)--Brigham Young University, 2018.
520 $a Computational creativity has been called the "final frontier" of artificial intelligence due to the difficulty inherent in defining and implementing creativity in computational systems. Despite this difficulty computer creativity is becoming a more significant part of our everyday lives, in particular music. This is observed in the prevalence of music recommendation systems, co-creational music software packages, smart playlists, and procedurally-generated video games. Significant progress can be seen in the advances in industrial applications such as Spotify, Pandora, Apple Music, etc., but several problems persist. Of more general interest, however, is the question of whether or not computers can exhibit autonomous creativity in music composition. One of the primary challenges in this endeavor is enabling computational systems to create music that exhibits global structure, that can learn structure from data, and which can effectively incorporate autonomy and intention.
520 $a We seek to address these challenges in the context of a modular machine learning framework called hierarchical Bayesian program learning (HBPL). Breaking the problem of music composition into smaller pieces, we focus primarily on developing machine learning models that solve the problems related to structure. In particular we present an adaptation of non-homogenous Markov models that enable binary constraints and we present a structural learning model, the multiple Smith-Waterman (mSW) alignment method, which extends sequence alignment techniques from bioinformatics. To address the issue of intention, we incorporate our work on structured sequence generation into a full-fledged computational creative system called Pop* which we show through various evaluative means to possess to varying extents the characteristics of creativity and also creativity itself.
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