384 Pages 64 B/W Illustrations
by CRC Press

384 Pages
by CRC Press

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... Read more

FUNDAMENTAL TOPICS
Music Data Mining: An Introduction, Tao Li and Lei Li
Audio Feature Extraction, George Tzanetakis

CLASSIFICATION
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

SOCIAL ASPECTS OF MUSIC DATA MINING
Web- and Community-Based Music Information Extraction, Markus Schedl
Indexing Music with Tags, Douglas Turnbull
Human Computation for Music Classification, Edith Law

ADVANCED TOPICS
Hit Song Science, Francois Pachet
Symbolic Data Mining in Musicology, Ian Knopke and Frauke Jurgensen

Index

Biography

Tao Li, Mitsunori Ogihara, George Tzanetakis

"… 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