Music Data Mining

Tao Li, Mitsunori Ogihara, George Tzanetakis

July 12, 2011 by CRC Press
Reference - 384 Pages - 64 B/W Illustrations
ISBN 9781439835524 - CAT# K11597
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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  • Covers cutting-edge research in music data mining and information retrieval
  • Presents a survey of music data mining, along with fundamental issues of classification, audio signal processing, and feature extraction
  • Describes new research in instrument recognition, mood and emotion classification, and hit song prediction science
  • Explores the social aspects of music, including the extraction of music information from the Web and peer-to-peer networks, the use of tags in music data mining, and human computation games
  • Provides contributions from leading experts in data mining, machine learning, and music science


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.


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