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Choral Music Generation: A Deep Hybr...

Szelogowski, Daniel James,

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Choral Music Generation: A Deep Hybrid Learning Approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Choral Music Generation: A Deep Hybrid Learning Approach // Daniel James Szelogowski.
Author:	Szelogowski, Daniel James,
Description:	1 electronic resource (301 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Music. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31296228
ISBN:	9798382798271

Choral Music Generation: A Deep Hybrid Learning Approach /
Szelogowski, Daniel James,

Choral Music Generation: A Deep Hybrid Learning Approach /Daniel James Szelogowski. - 1 electronic resource (301 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Despite advancements in AI and machine learning, the creation of AI-generated music that captures the complexity and nuance of classical choral arrangements has remained largely unexplored. This gap is significant, considering the intricate compositional techniques and sophisticated music theory knowledge required for classical choral music. My research is motivated by the quest to bridge this gap, aiming to develop an AI model capable of producing realistic choral compositions that adhere to the rich traditions of classical music. Additionally, I explore how AI-generated music is perceived across different listener groups, contributing insights into the intersection of AI and human perception in the arts. This study seeks to address three primary research questions: the feasibility of current machine learning architectures in generating SATB choral music, the ability of a hybrid AI model to produce compositions indistinguishable from human-created music by the general public, and the varying perception of AI-generated classical music among individuals with varying musical backgrounds. This dissertation presents the "Choral-GTN" system: a novel architecture combining a Generative Transformer Network with a rule-based post-processing system, alongside the curated "CHORAL" dataset to address the challenge of generating realistic four-part (SATB) choral music. My comprehensive approach integrates advanced deep learning techniques with sophisticated musical theory insights to produce compositions that closely mimic those created by human composers. Through a meticulously designed survey and rigorous statistical analysis, I evaluate the realism and indistinguishability of my AI-generated music across varied listener groups. The findings demonstrate that this model successfully created music that was largely indistinguishable from human compositions, achieving a significant milestone in AI-assisted music composition and music perception.

English

ISBN: 9798382798271Subjects--Topical Terms:

516178
Music.
Subjects--Index Terms:

Choral music

Choral Music Generation: A Deep Hybrid Learning Approach /
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245 1 0 $a Choral Music Generation: A Deep Hybrid Learning Approach / $c Daniel James Szelogowski.
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500 $a Advisors: Goodwin, Susan; McAndrew, Ian Committee members: Bodily, Paul; Noir, Sophia.
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520 $a Despite advancements in AI and machine learning, the creation of AI-generated music that captures the complexity and nuance of classical choral arrangements has remained largely unexplored. This gap is significant, considering the intricate compositional techniques and sophisticated music theory knowledge required for classical choral music. My research is motivated by the quest to bridge this gap, aiming to develop an AI model capable of producing realistic choral compositions that adhere to the rich traditions of classical music. Additionally, I explore how AI-generated music is perceived across different listener groups, contributing insights into the intersection of AI and human perception in the arts. This study seeks to address three primary research questions: the feasibility of current machine learning architectures in generating SATB choral music, the ability of a hybrid AI model to produce compositions indistinguishable from human-created music by the general public, and the varying perception of AI-generated classical music among individuals with varying musical backgrounds. This dissertation presents the "Choral-GTN" system: a novel architecture combining a Generative Transformer Network with a rule-based post-processing system, alongside the curated "CHORAL" dataset to address the challenge of generating realistic four-part (SATB) choral music. My comprehensive approach integrates advanced deep learning techniques with sophisticated musical theory insights to produce compositions that closely mimic those created by human composers. Through a meticulously designed survey and rigorous statistical analysis, I evaluate the realism and indistinguishability of my AI-generated music across varied listener groups. The findings demonstrate that this model successfully created music that was largely indistinguishable from human compositions, achieving a significant milestone in AI-assisted music composition and music perception.
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650 4 $a Computer science. $3 523869
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653 $a Choral music
653 $a Computational musicology
653 $a Deep learning
653 $a Music generation
653 $a Music perception
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690 $a 0413
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690 $a 0214
710 2 $a Capitol Technology University. $b Technology (PhD). $e degree granting institution. $3 3815626
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720 1 $a McAndrew, Ian $e degree supervisor.
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