Signal Processing Methods for Music Transcription

Manuel Davy (u. a.)

ISBN 10: 1441940359 / ISBN 13: 9781441940353

Published by Springer US, 2010

About this Item

Signal Processing Methods for Music Transcription | Manuel Davy (u. a.) | Taschenbuch | xii | Englisch | 2010 | Springer US | EAN 9781441940353 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 107252299

Bibliographic Details

• Title: Signal Processing Methods for Music Transcription
• Publisher: Springer US
• Publication Date: 2010
• Language: English
• ISBN 10: 1441940359
• ISBN 13: 9781441940353
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 25D millimeters 235 millimeters width by 155 millimeters height by 25 millimeters depth
• Weight: 680 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

From the Back Cover

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.