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Computational music analysis

Meredith, David.

Computational music analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational music analysisedited by David Meredith.
其他作者:	Meredith, David.
出版者:	Cham :Springer International Publishing :2016.
面頁冊數:	xv, 480 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	MusicMathematics.
電子資源:	http://dx.doi.org/10.1007/978-3-319-25931-4
ISBN:	9783319259314$q(electronic bk.)

Computational music analysis
Computational music analysis[electronic resource] /edited by David Meredith. - Cham :Springer International Publishing :2016. - xv, 480 p. :ill., digital ;24 cm.

Music Analysis by Computer Ontology and Epistemology -- The Harmonic Musical Surface and Two Novel Chord Representation Schemes -- Topological Structures in Computer-Aided Music Analysis -- Contextual Set-Class Analysis -- Computational Analysis of Musical Form -- Chord- and Note-Based Approaches to Voice Separation -- Analysing Symbolic Music with Probabilistic Grammars -- Interactive Melodic Analysis -- Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff's Generative Theory of Tonal Music -- An Algebraic Approach to Time-Span Reduction -- Automated Motivic Analysis An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces -- A Wavelet-Based Approach to Pattern Discovery in Melodies -- Analysing Music with Point-Set Compression Algorithms -- Composer Classification Models for Music-Theory Building -- Contrast Pattern Mining in Folk Music Analysis -- Pattern and Antipattern Discovery in Ethiopian Bagana Songs -- Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora -- Index.

This book provides an in-depth introduction and overview of current research in computational music analysis. Its seventeen chapters, written by leading researchers, collectively represent the diversity as well as the technical and philosophical sophistication of the work being done today in this intensely interdisciplinary field. A broad range of approaches are presented, employing techniques originating in disciplines such as linguistics, information theory, information retrieval, pattern recognition, machine learning, topology, algebra and signal processing. Many of the methods described draw on well-established theories in music theory and analysis, such as Forte's pitch-class set theory, Schenkerian analysis, the methods of semiotic analysis developed by Ruwet and Nattiez, and Lerdahl and Jackendoff's Generative Theory of Tonal Music. The book is divided into six parts, covering methodological issues, harmonic and pitch-class set analysis, form and voice-separation, grammars and hierarchical reduction, motivic analysis and pattern discovery and, finally, classification and the discovery of distinctive patterns. As a detailed and up-to-date picture of current research in computational music analysis, the book provides an invaluable resource for researchers, teachers and students in music theory and analysis, computer science, music information retrieval and related disciplines. It also provides a state-of-the-art reference for practitioners in the music technology industry.

ISBN: 9783319259314$q(electronic bk.)

Standard No.: 10.1007/978-3-319-25931-4doiSubjects--Topical Terms:

737289
Music
--Mathematics.

LC Class. No.: ML3800 / .C66 2016

Dewey Class. No.: 780.1

Computational music analysis
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520 $a This book provides an in-depth introduction and overview of current research in computational music analysis. Its seventeen chapters, written by leading researchers, collectively represent the diversity as well as the technical and philosophical sophistication of the work being done today in this intensely interdisciplinary field. A broad range of approaches are presented, employing techniques originating in disciplines such as linguistics, information theory, information retrieval, pattern recognition, machine learning, topology, algebra and signal processing. Many of the methods described draw on well-established theories in music theory and analysis, such as Forte's pitch-class set theory, Schenkerian analysis, the methods of semiotic analysis developed by Ruwet and Nattiez, and Lerdahl and Jackendoff's Generative Theory of Tonal Music. The book is divided into six parts, covering methodological issues, harmonic and pitch-class set analysis, form and voice-separation, grammars and hierarchical reduction, motivic analysis and pattern discovery and, finally, classification and the discovery of distinctive patterns. As a detailed and up-to-date picture of current research in computational music analysis, the book provides an invaluable resource for researchers, teachers and students in music theory and analysis, computer science, music information retrieval and related disciplines. It also provides a state-of-the-art reference for practitioners in the music technology industry.
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