Music Data Mining

Advances in digital music technology have created challenges for effectively accessing and interacting with large collections of music and associated data. This book explores how data mining addresses these challenges by mining useful information and using it to create novel ways of interacting with large music collections. Leading experts in data mining, machine learning, and music science examine fundamental issues of classification and audio signal processing and discuss social aspects of music mining. They also present new research in instrument recognition, mood and emotion classification, and hit song prediction science.

Tao Li, Mitsunori Ogihara, George Tzanetakis

FUNDAMENTAL TOPICS: Music Data Mining: An Introduction. Audio Feature Extraction. CLASSIFICATION: Auditory Sparse Coding. Instrument Recognition. Mood and Emotional Classification. Zipf's Law, Power Laws and Music Aesthetics. SOCIAL ASPECTS OF MUSIC DATA MINING: Web- and Community-Based Music Information Extraction. Indexing Music with Tags. Human Computation for Music Classification. ADVANCED TOPICS: Hit Song Science. Symbolic Data Mining in Musicology. Index.