東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Music Information Geometry - Algebra...

Steinmetz, Shannon,

Linked to FindBook

Google Book

Amazon

博客來

Music Information Geometry - Algebraic Group, Metric, and Equivalence for A-Priori, Real-Time Sensation of Audio /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Music Information Geometry - Algebraic Group, Metric, and Equivalence for A-Priori, Real-Time Sensation of Audio // Shannon Steinmetz.
Author:	Steinmetz, Shannon,
Description:	1 electronic resource (315 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: A.
Contained By:	Dissertations Abstracts International86-11A.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997773
ISBN:	9798314855126

Music Information Geometry - Algebraic Group, Metric, and Equivalence for A-Priori, Real-Time Sensation of Audio /
Steinmetz, Shannon,

Music Information Geometry - Algebraic Group, Metric, and Equivalence for A-Priori, Real-Time Sensation of Audio /Shannon Steinmetz. - 1 electronic resource (315 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: A.

Computer vision allows robots to see the world, which means computer-hearing is the next stage of robotic evolution where machines can sympathize with the sensations of musical signals. To accomplish this, machines must have a way to objectively measure interactions between musical tones in real-time, a-priori. Music theory allows for analyzing musical tones and their transition with sets, graphs, geometries and parsimonious relations. The challenge we face from traditional theory is insufficient representation of multiple frequencies that arise due to density in real-time signals. This includes artifacts from timbre, ambience, noise, overtones, combination tones, human error, equipment, percussion, etc. In this dissertation we bridge the gap between mathematical methods of at-rest symbology in traditional theory and in-motion modeling of music sensation. We propose music information geometry (MIG) as a theoretical basis for holistic modeling. The proposed framework is geared toward analysis of audio signals by encoding every possible musical permutation. The central claim is; a map between arbitrary time-series (musical audio) and time-geometry exists as a set of analytic geometries. The set constitutes an additive Abelian group (G,+), metric and equivalence class for modeling music sensation. We demonstrate this claim under the assumption of an ideal-musical-quantifier and mathematical proof. Furthermore, we provide evidence of group efficacy by proof-of-concept and implementations of non-ideal musical quantifiers in centricity, harmonic consistency, conjunct melodic motion and acoustic consonance from The Geometry of Music. We experiment using custom, synthesized and standardized data to gather evidence to show instrumental value of the group. We provide evidence MIG is useful in multiple circumstances to include data sonification, classification of genre, and music/non-music content recognition. We contribute signal processing methods, including mean energy noise cancellation (MENCA) for attenuating peak picking in noisy audio.

English

ISBN: 9798314855126Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computer hearing

Music Information Geometry - Algebraic Group, Metric, and Equivalence for A-Priori, Real-Time Sensation of Audio /
LDR:03701nmm a22004693i 4500 001 2436944
005 20251117053659.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798314855126
035 $a (MiAaPQD)AAI31997773
035 $a AAI31997773
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Steinmetz, Shannon, $e author. $3 3827783
245 1 0 $a Music Information Geometry - Algebraic Group, Metric, and Equivalence for A-Priori, Real-Time Sensation of Audio / $c Shannon Steinmetz.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (315 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-11, Section: A.
500 $a Advisors: Gethner, Ellen; Jafarian, Haadi Committee members: Gethner, Ellen; Alaghaband, Gita; Al Borno, Mazen; Wu, Cecilia.
502 $b Ph.D. $c University of Colorado at Denver $d 2025.
520 $a Computer vision allows robots to see the world, which means computer-hearing is the next stage of robotic evolution where machines can sympathize with the sensations of musical signals. To accomplish this, machines must have a way to objectively measure interactions between musical tones in real-time, a-priori. Music theory allows for analyzing musical tones and their transition with sets, graphs, geometries and parsimonious relations. The challenge we face from traditional theory is insufficient representation of multiple frequencies that arise due to density in real-time signals. This includes artifacts from timbre, ambience, noise, overtones, combination tones, human error, equipment, percussion, etc. In this dissertation we bridge the gap between mathematical methods of at-rest symbology in traditional theory and in-motion modeling of music sensation. We propose music information geometry (MIG) as a theoretical basis for holistic modeling. The proposed framework is geared toward analysis of audio signals by encoding every possible musical permutation. The central claim is; a map between arbitrary time-series (musical audio) and time-geometry exists as a set of analytic geometries. The set constitutes an additive Abelian group (G,+), metric and equivalence class for modeling music sensation. We demonstrate this claim under the assumption of an ideal-musical-quantifier and mathematical proof. Furthermore, we provide evidence of group efficacy by proof-of-concept and implementations of non-ideal musical quantifiers in centricity, harmonic consistency, conjunct melodic motion and acoustic consonance from The Geometry of Music. We experiment using custom, synthesized and standardized data to gather evidence to show instrumental value of the group. We provide evidence MIG is useful in multiple circumstances to include data sonification, classification of genre, and music/non-music content recognition. We contribute signal processing methods, including mean energy noise cancellation (MENCA) for attenuating peak picking in noisy audio.
546 $a English
590 $a School code: 0765
650 4 $a Computer science. $3 523869
650 4 $a Mathematics. $3 515831
650 4 $a Music. $3 516178
650 4 $a Acoustics. $3 879105
653 $a Computer hearing
653 $a Geometry of Music
653 $a MENCA
653 $a Music information geometry
653 $a Musical signal processing
653 $a Sonification
690 $a 0984
690 $a 0405
690 $a 0413
690 $a 0986
710 2 $a University of Colorado at Denver. $b Engineering and Applied Science. $e degree granting institution. $3 3826637
720 1 $a Gethner, Ellen $e degree supervisor.
720 1 $a Jafarian, Haadi $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-11A.
790 $a 0765
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997773