Table of contents

  1. Front Matter
    Pages i-xv
  2. Methodology

    1. Front Matter
      Pages 1-1
  3. Chords and Pitch Class Sets

    1. Front Matter
      Pages 29-29
    2. Louis Bigo, Moreno Andreatta
      Pages 57-80
    3. Agustín Martorell, Emilia Gómez
      Pages 81-110
  4. Parsing Large-Scale Structure: Form and Voice-Separation

    1. Front Matter
      Pages 111-111
    2. Mathieu Giraud, Richard Groult, Florence Levé
      Pages 113-136
    3. Tillman Weyde, Reinier de Valk
      Pages 137-154
  5. Grammars and Hierarchical Structure

    1. Front Matter
      Pages 155-155
    2. Samer Abdallah, Nicolas Gold, Alan Marsden
      Pages 157-189
    3. David Rizo, Plácido R. Illescas, José M. Iñesta
      Pages 191-219
    4. Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka
      Pages 251-270
  6. Motivic and Thematic Analysis

  7. Classification and Distinctive Patterns

    1. Front Matter
      Pages 367-367

About this book

Introduction

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.

Keywords

Algorithmic Composition Algorithms Computational Music Analysis Computer Music Machine Learning Mathematics and Music Music Analysis Music Cognition Music Information Retrieval Music Perception Music Theory

Editors and affiliations

  • David Meredith
    • 1
  1. 1.Dept. of Arch., Design & Media TechAalborg UniversityAalborgDenmark

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-25931-4
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-25929-1
  • Online ISBN 978-3-319-25931-4
  • About this book