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New book from Springer on Computational Music Analysis



[Apologies for cross-posting!]

Dear list members,

I'm delighted to announce the online publication of a new book from Springer, entitled "Computational Music Analysis". The book is available through Springerlink at the following URL:

http://link.springer.com/book/10.1007/978-3-319-25931-4

The book is also available for pre-order on Amazon at the following URLs:

http://www.amazon.co.uk/Computational-Music-Analysis-David-Meredith/dp/3319259296
http://www.amazon.com/Computational-Music-Analysis-David-Meredith/dp/3319259296
http://www.amazon.de/Computational-Music-Analysis-David-Meredith/dp/3319259296
http://www.amazon.fr/Computational-Music-Analysis-David-Meredith/dp/3319259296

The book contains 17 chapters representing the following general areas:

- Methodology
- Chords and pitch class sets
- Parsing large-scale structure: Form and voice-separation
- Grammars and hierarchical structure
- Motivic and thematic analysis
- Classification and distinctive patterns

I include below the text from the back cover, along with a list of chapters.

Kind regards,
David Meredith

__________
From the back cover:

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.

___________

List of chapters (please excuse lack of accents)

Part I Methodology

1 Music Analysis by Computer: Ontology and Epistemology
Alan Marsden

Part II Chords and Pitch Class Sets

2 The Harmonic Musical Surface and Two Novel Chord Representation Schemes
Emilios Cambouropoulos

3 Topological Structures in Computer-Aided Music Analysis
Louis Bigo and Moreno Andreatta

4 Contextual Set-Class Analysis
Agustin Martorell and Emilia Gomez

Part III Parsing Large-Scale Structure: Form and Voice-Separation

5 Computational Analysis of Musical Form
Mathieu Giraud, Richard Groult, and Florence Leve

6 Chord- and Note-Based Approaches to Voice Separation
Tillman Weyde and Reinier de Valk

Part IV Grammars and Hierarchical Structure

7 Analysing Symbolic Music with Probabilistic Grammars
Samer Abdallah, Nicolas Gold, and Alan Marsden

8 Interactive Melodic Analysis
David Rizo, Placido R. Illescas, and Jose M. Inesta

9 Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music
Masatoshi Hamanaka, Keiji Hirata, and Satoshi Tojo

10 An Algebraic Approach to Time-Span Reduction
Keiji Hirata, Satoshi Tojo, and Masatoshi Hamanaka

Part V Motivic and Thematic Analysis

11 Automated Motivic Analysis: An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces
Olivier Lartillot

12 A Wavelet-Based Approach to Pattern Discovery in Melodies
Gissel Velarde, David Meredith, and Tillman Weyde

13 Analysing Music with Point-Set Compression Algorithms
David Meredith

Part VI Classification and Distinctive Patterns

14 Composer Classification Models for Music-Theory Building
Dorien Herremans, David Martens, and Kenneth Sorensen

15 Contrast Pattern Mining in Folk Music Analysis
Kerstin Neubarth and Darrell Conklin

16 Pattern and Antipattern Discovery in Ethiopian Bagana Songs
Darrell Conklin and Stephanie Weisser

17 Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora
Tom Collins, Andreas Arzt, Harald Frostel, and Gerhard Widmer