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Titlebook: Signal Processing Methods for Music Transcription; Anssi Klapuri,Manuel Davy Book 2006 Springer-Verlag US 2006 Acoustics.algorithms.classi

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書目名稱Signal Processing Methods for Music Transcription
編輯Anssi Klapuri,Manuel Davy
視頻videohttp://file.papertrans.cn/868/867065/867065.mp4
概述The first book uniting state-of-the-art research in signal processing for music transcription.Covers a range of topics and approaches discussed by international experts in the field.Contributes to the
圖書封面Titlebook: Signal Processing Methods for Music Transcription;  Anssi Klapuri,Manuel Davy Book 2006 Springer-Verlag US 2006 Acoustics.algorithms.classi
描述.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..
出版日期Book 2006
關(guān)鍵詞Acoustics; algorithms; classification; cognition; learning; modeling; signal processing
版次1
doihttps://doi.org/10.1007/0-387-32845-9
isbn_softcover978-1-4419-4035-3
isbn_ebook978-0-387-32845-4
copyrightSpringer-Verlag US 2006
The information of publication is updating

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An Introduction to Statistical Signal Processing and Spectrum Estimationsity functions, and likelihood functions. It also introduces estimation theory. Section 2.3 is about Bayesian estimation methods, including Monte Carlo techniques for numerical computations. Finally, Section 2.4 introduces pattern recognition methods, including support vector machines and hidden Markov models.
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Auditory Model-Based Methods for Multiple Fundamental Frequency Estimatione natural to pursue automatic music transcription and multiple FO estimation by investigating what happens in the human listener. Here the term multiple FO estimation means estimating the FOs of several concurrent sounds.
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Multiple Fundamental Frequency Estimation Based on Generative Models[451], [193]. Though acoustic waveforms may vary from one musical instrument to another, and even from one performance to another with the same instrument, they can be modelled accurately using a unique mathematical model, with different parameters.
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