Computational Music Analysis

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David Meredith
Springer, Oct 27, 2015 - Computers - 480 pages
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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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Contents

Ontology and Epistemology
3
Part II Chords and Pitch Class Sets
29
2 The Harmonic Musical Surface and Two Novel Chord Representation Schemes
31
3 Topological Structures in ComputerAided Music Analysis
57
4 Contextual SetClass Analysis
81
Form and VoiceSeparation
111
5 Computational Analysis of Musical Form
112
6 Chord and NoteBased Approaches to Voice Separation
137
10 An Algebraic Approach to TimeSpan Reduction
250
Part V Motivic and Thematic Analysis
271
An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces
273
12 A WaveletBased Approach to Pattern Discovery in Melodies
303
13 Analysing Music with PointSet Compression Algorithms
334
Part VI Classification and Distinctive Patterns
367
14 Composer Classification Models for MusicTheory Building
369
15 Contrast Pattern Mining in Folk Music Analysis
393

Part IV Grammars and Hierarchical Structure
155
7 Analysing Symbolic Music with Probabilistic Grammars
157
8 Interactive Melodic Analysis
190
9 Implementing Methods for Analysing Music Based on Lerdahl and Jackendoffs Generative Theory of Tonal Music
221
16 Pattern and Antipattern Discovery in Ethiopian Bagana Songs
425
17 Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora
444
Index
475
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About the author (2015)

David Meredith is an Associate Professor in the Dept. of Architecture, Design and Media Technology at Aalborg University. He has Bachelor's and Master's degrees in natural sciences and music from the University of Cambridge and a D.Phil. from the Faculty of Music of the University of Oxford. His research focuses on algorithms for analysing musical structure. He developed the first practical algorithms for discovering repeated patterns in polyphonic music and the most accurate pitch spelling algorithm to date. He is the lead investigator at Aalborg University on the EU collaborative project, "Learning to Create" (Lrn2Cre8).

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