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Visualizing Temporality in Music: Mu...

Hamidi Ghalehjegh, Nima.

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Visualizing Temporality in Music: Music Perception - Feature Extraction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visualizing Temporality in Music: Music Perception - Feature Extraction./
Author:	Hamidi Ghalehjegh, Nima.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	184 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: A.
Contained By:	Dissertation Abstracts International79-02A(E).
Subject:	Music. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10603043
ISBN:	9780355244830

Visualizing Temporality in Music: Music Perception - Feature Extraction.
Hamidi Ghalehjegh, Nima.

Visualizing Temporality in Music: Music Perception - Feature Extraction. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 184 p.

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: A.

Thesis (Ph.D.)--The University of Iowa, 2017.

Recently, there have been efforts to design more efficient ways to internalize music by applying the disciplines of cognition, psychology, temporality, aesthetics, and philosophy. Bringing together the fields of art and science, computational techniques can also be applied to musical analysis. Although a wide range of research projects have been conducted, the automatization of music analysis remains emergent. Importantly, patterns are revealed by using automated tools to analyze core musical elements created from melodies, harmonies, and rhythms, high-level features that are perceivable by the human ear. For music to be captured and successfully analyzed by a computer, however, one needs to extract certain information found in the lower-level features of amplitude, frequency, and duration. Moreover, while the identity of harmonic progressions, melodic contour, musical patterns, and pitch quantification are crucial factors in traditional music analysis, these alone are not exclusive. Visual representations are useful tools that reflect form and structure of non-conventional musical repertoire.

ISBN: 9780355244830Subjects--Topical Terms:

516178
Music.

Visualizing Temporality in Music: Music Perception - Feature Extraction.
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: A.
500 $a Adviser: David Gompper.
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520 $a Recently, there have been efforts to design more efficient ways to internalize music by applying the disciplines of cognition, psychology, temporality, aesthetics, and philosophy. Bringing together the fields of art and science, computational techniques can also be applied to musical analysis. Although a wide range of research projects have been conducted, the automatization of music analysis remains emergent. Importantly, patterns are revealed by using automated tools to analyze core musical elements created from melodies, harmonies, and rhythms, high-level features that are perceivable by the human ear. For music to be captured and successfully analyzed by a computer, however, one needs to extract certain information found in the lower-level features of amplitude, frequency, and duration. Moreover, while the identity of harmonic progressions, melodic contour, musical patterns, and pitch quantification are crucial factors in traditional music analysis, these alone are not exclusive. Visual representations are useful tools that reflect form and structure of non-conventional musical repertoire.
520 $a Because I regard the fluidity of music and visual shape as strongly interactive, the ultimate goal of this thesis is to construct a practical tool that prepares the visual material used for musical composition. By utilizing concepts of time, computation, and composition, this tool effectively integrates computer science, signal processing, and music perception. This will be obtained by presenting two concepts, one abstract and one mathematical, that will provide materials leading to the original composition. To extract the desired visualization, I propose a fully automated tool for musical analysis that is grounded in both the mid-level elements of loudness, density, and range, and low-level features of frequency and duration. As evidenced by my sinfonietta, Equilibrium, this tool, capable of rapidly analyzing a variety of musical examples such as instrumental repertoire, electro-acoustic music, improvisation and folk music, is highly beneficial to my proposed compositional procedure.
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