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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 classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining.
The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.
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 classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining.
The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.
目次
FUNDAMENTAL TOPICSMusic Data Mining: An Introduction, Tao Li and Lei LiAudio Feature Extraction, George Tzanetakis
CLASSIFICATIONAuditory Sparse Coding, Steven R. Ness, Thomas C. Walters, and Richard F. LyonInstrument Recognition, Jayme Garcia Arnal BarbedoMood and Emotional Classification, Mitsunori Ogihara and Youngmoo KimZipf’s Law, Power Laws, and Music Aesthetics, Bill Manaris, Patrick Roos, Dwight Krehbiel, Thomas Zalonis, and J.R. Armstrong
SOCIAL ASPECTS OF MUSIC DATA MININGWeb- and Community-Based Music Information Extraction, Markus SchedlIndexing Music with Tags, Douglas TurnbullHuman Computation for Music Classification, Edith Law
ADVANCED TOPICSHit Song Science, Francois PachetSymbolic Data Mining in Musicology, Ian Knopke and Frauke Jurgensen
Index
CLASSIFICATIONAuditory Sparse Coding, Steven R. Ness, Thomas C. Walters, and Richard F. LyonInstrument Recognition, Jayme Garcia Arnal BarbedoMood and Emotional Classification, Mitsunori Ogihara and Youngmoo KimZipf’s Law, Power Laws, and Music Aesthetics, Bill Manaris, Patrick Roos, Dwight Krehbiel, Thomas Zalonis, and J.R. Armstrong
SOCIAL ASPECTS OF MUSIC DATA MININGWeb- and Community-Based Music Information Extraction, Markus SchedlIndexing Music with Tags, Douglas TurnbullHuman Computation for Music Classification, Edith Law
ADVANCED TOPICSHit Song Science, Francois PachetSymbolic Data Mining in Musicology, Ian Knopke and Frauke Jurgensen
Index
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