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|a 1 online resource (xxiv, 360 pages) :
|b illustrations
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|a Chapman & Hall/CRC data mining and knowledge discovery series
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|a Includes bibliographical references and index.
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|a Music data mining: an introduction / Tao Li and Lei Li Audio feature extraction / George Tzanetakis Auditory sparse coding / Steven R. Ness, Thomas C. Walters, and Richard F. Lyon -- Instrument recognition / Jayme Garcia Arnal Barbedo -- Mood and emotional classification / Mitsunori Ogihara and Youngmoo Kim -- Zipf's law, power laws, and music aesthetics / Bill Manaris [and others] -- Web-based and community-based music information extraction / Markus Schedl -- Indexing music with tags / Douglas Turnbull -- Human computation for music classification / Edith Law -- Hit song science / François Pachet -- Symbolic data mining in musicology / Ian Knopke and Frauke Jürgensen.
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|a Print version record.
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|a The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to successfully employ data mining techniques for the purpose of music processing.The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classificat.
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|a Musical analysis
|x Data processing.
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|a Data mining.
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|a Information storage and retrieval systems
|x Music.
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2 |
|a Data Mining
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|a Analyse musicale
|x Informatique.
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|a Exploration de données (Informatique)
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|a Systèmes d'information
|x Musique.
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|a Li, Tao.
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|a Ogihara, Mitsunori,
|d 1963-
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|a Tzanetakis, George,
|d 1975-
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|i Print version:
|t Music data mining.
|d Boca Raton : CRC Press, ©2012
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