Signal Processing Methods for Music Transcription [electronic resource] / edited by Anssi Klapuri, Manuel Davy.

By: Klapuri, Anssi [editor.]Contributor(s): Davy, Manuel [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Publisher: Boston, MA : Springer US, 2006Description: XII, 440p. 124 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780387328454Subject(s): Engineering | Information storage and retrieval systems | Translators (Computer programs) | Optical pattern recognition | Engineering | Signal, Image and Speech Processing | Pattern Recognition | Information Storage and Retrieval | Language Translation and LinguisticsAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 LOC classification: TK5102.9TA1637-1638TK7882.S65Online resources: Click here to access online
Contents:
Foundations -- to Music Transcription -- An Introduction to Statistical Signal Processing and Spectrum Estimation -- Sparse Adaptive Representations for Musical Signals -- Rhythm and Timbre Analysis -- Beat Tracking and Musical Metre Analysis -- Unpitched Percussion Transcription -- Automatic Classification of Pitched Musical Instrument Sounds -- Multiple Fundamental Frequency Analysis -- Multiple Fundamental Frequency Estimation Based on Generative Models -- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation -- Unsupervised Learning Methods for Source Separation in Monaural Music Signals -- Entire Systems, Acoustic and Musicological Modelling -- Auditory Scene Analysis in Music Signals -- Music Scene Description -- Singing Transcription.
In: Springer eBooksSummary: 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.
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Foundations -- to Music Transcription -- An Introduction to Statistical Signal Processing and Spectrum Estimation -- Sparse Adaptive Representations for Musical Signals -- Rhythm and Timbre Analysis -- Beat Tracking and Musical Metre Analysis -- Unpitched Percussion Transcription -- Automatic Classification of Pitched Musical Instrument Sounds -- Multiple Fundamental Frequency Analysis -- Multiple Fundamental Frequency Estimation Based on Generative Models -- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation -- Unsupervised Learning Methods for Source Separation in Monaural Music Signals -- Entire Systems, Acoustic and Musicological Modelling -- Auditory Scene Analysis in Music Signals -- Music Scene Description -- Singing Transcription.

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.

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