Music Data Mining: 1st Edition (Hardback) book cover

Music Data Mining

1st Edition

Edited by Tao Li, Mitsunori Ogihara, George Tzanetakis

CRC Press

384 pages | 64 B/W Illus.

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pub: 2011-07-12
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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.


"… a useful survey for the reader specifically interested in MIR."

Statistical Papers (2013) 54

"This book, as a collection of papers, brings together some of the leading scholars of the field to tackle a number of data mining techniques aiming mainly at data classification."

—Joonas Kauppinen, International Statistical Review, 2012

Table of Contents


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, Patrick Roos, Dwight Krehbiel, Thomas Zalonis, and J.R. Armstrong


Web- and Community-Based Music Information Extraction, Markus Schedl

Indexing Music with Tags, Douglas Turnbull

Human Computation for Music Classification, Edith Law


Hit Song Science, Francois Pachet

Symbolic Data Mining in Musicology, Ian Knopke and Frauke Jurgensen


About the Originator

About the Series

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Programming / Games
COMPUTERS / Database Management / Data Mining
COMPUTERS / Machine Theory