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Computational Music Analysis

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Cover of 'Computational Music Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Music Analysis by Computer: Ontology and Epistemology
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    Chapter 2 The Harmonic Musical Surface and Two Novel Chord Representation Schemes
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    Chapter 3 Topological Structures in Computer-Aided Music Analysis
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    Chapter 4 Contextual Set-Class Analysis
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    Chapter 5 Computational Analysis of Musical Form
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    Chapter 6 Chord- and Note-Based Approaches to Voice Separation
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    Chapter 7 Analysing Symbolic Music with Probabilistic Grammars
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    Chapter 8 Interactive Melodic Analysis
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    Chapter 9 Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music
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    Chapter 10 An Algebraic Approach to Time-Span Reduction
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    Chapter 11 Automated Motivic Analysis: An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces
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    Chapter 12 A Wavelet-Based Approach to Pattern Discovery in Melodies
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    Chapter 13 Analysing Music with Point-Set Compression Algorithms
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    Chapter 14 Composer Classification Models for Music-Theory Building
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    Chapter 15 Contrast Pattern Mining in Folk Music Analysis
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    Chapter 16 Pattern and Antipattern Discovery in Ethiopian Bagana Songs
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    Chapter 17 Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora
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Title
Computational Music Analysis
Published by
Springer International Publishing, January 2016
DOI 10.1007/978-3-319-25931-4
ISBNs
978-3-31-925929-1, 978-3-31-925931-4
Editors

David Meredith

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Italy 1 <1%
Brazil 1 <1%
Unknown 100 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 16%
Student > Ph. D. Student 15 15%
Student > Bachelor 13 13%
Researcher 8 8%
Student > Postgraduate 7 7%
Other 19 18%
Unknown 25 24%
Readers by discipline Count As %
Computer Science 30 29%
Arts and Humanities 20 19%
Engineering 10 10%
Psychology 5 5%
Mathematics 2 2%
Other 8 8%
Unknown 28 27%