Signal Processing Methods for Music Transcription

  • Anssi Klapuri
  • Manuel Davy

Table of contents

  1. Front Matter
    Pages i-xii
  2. Foundations

    1. Front Matter
      Pages 1-1
    2. Anssi Klapuri
      Pages 3-20
    3. Laurent Daudet, Bruno Torrésani
      Pages 65-98
  3. Rhythm and Timbre Analysis

    1. Front Matter
      Pages 99-99
    2. Stephen Hainsworth
      Pages 101-129
    3. Derry FitzGerald, Jouni Paulus
      Pages 131-162
    4. Perfecto Herrera-Boyer, Anssi Klapuri, Manuel Davy
      Pages 163-200
  4. Multiple Fundamental Frequency Analysis

  5. Entire Systems, Acoustic and Musicological Modelling

    1. Front Matter
      Pages 297-297
    2. Kunio Kashino
      Pages 299-325
    3. Masataka Goto
      Pages 327-359
    4. Matti Ryynänen
      Pages 361-390
  6. Back Matter
    Pages 391-440

About this book


Signal Processing Methods for Music Transcription is the first book dedicated to uniting research related to signal processing algorithms and models for various aspects of music transcription such as pitch analysis, rhythm analysis, percussion transcription, source separation, instrument recognition, and music structure analysis. Following a clearly structured pattern, each chapter provides a comprehensive review of the existing methods for a certain subtopic while covering the most important state-of-the-art methods in detail. The concrete algorithms and formulas are clearly defined and can be easily implemented and tested. A number of approaches are covered, including, for example, statistical methods, perceptually-motivated methods, and unsupervised learning methods. The text is enhanced by a common reference and index.

This book aims to serve as an ideal starting point for newcomers and an excellent reference source for people already working in the field. Researchers and graduate students in signal processing, computer science, acoustics and music will primarily benefit from this text. It could be used as a textbook for advanced courses in music signal processing. Since it only requires a basic knowledge of signal processing, it is accessible to undergraduate students.


Acoustics algorithms classification cognition learning modeling signal processing

Editors and affiliations

  • Anssi Klapuri
    • 1
  • Manuel Davy
    • 2
  1. 1.Institute of Signal ProcessingTampere University of TechnologyTampereFinland
  2. 2.Ecole Centrale de Lille Cité ScientifiqueLAGIS/CNRSCedexFrance

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media LLC 2006
  • Publisher Name Springer, Boston, MA
  • eBook Packages Engineering
  • Print ISBN 978-0-387-30667-4
  • Online ISBN 978-0-387-32845-4
  • Buy this book on publisher's site
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