東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine Learning for Inspired, Struc...

Bodily, Paul Mark.

FindBook

Google Book

Amazon

博客來

Machine Learning for Inspired, Structured, Lyrical Music Composition.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning for Inspired, Structured, Lyrical Music Composition./
作者:	Bodily, Paul Mark.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	152 p.
附註:	Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Contained By:	Dissertation Abstracts International80-02B(E).
標題:	Computer science. -
電子資源:	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.
LDR:02806nmm a2200313 4500 001 2201323
005 20190429062346.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438458017
035 $a (MiAaPQ)AAI10845187
035 $a (MiAaPQ)byu:10850
035 $a AAI10845187
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bodily, Paul Mark. $0 (orcid)0000-0002-5941-0069 $3 3428050
245 1 0 $a Machine Learning for Inspired, Structured, Lyrical Music Composition.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
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.
590 $a School code: 0022.
650 4 $a Computer science. $3 523869
650 4 $a Musical composition. $3 3289630
690 $a 0984
690 $a 0214
710 2 $a Brigham Young University. $b Computer Science. $3 1670078
773 0 $t Dissertation Abstracts International $g 80-02B(E).
790 $a 0022
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10845187