Mining in Music Databases

Mining in Music Databases

Ioannis Karydis (Aristotle University of Thessaloniki, Greece), Alexandros Nanopoulos (University of Thessaloniki, Greece) and Yannis Manolopoulos (Aristotle University of Thessaloniki, Greece)
DOI: 10.4018/978-1-59904-951-9.ch224
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Abstract

This chapter provides a broad survey of music data mining, including clustering, classification and pattern discovery in music. The data studied is mainly symbolic encodings of musical scores, although digital audio (acoustic data) is also addressed. Throughout the chapter, practical applications of music data mining are presented. Music data mining addresses the discovery of knowledge from music corpora. This chapter encapsulates the theory and methods required in order to discover knowledge in the form of patterns for music analysis and retrieval, or statistical models for music classification and generation. Music data, with their temporal, highly structured and polyphonic character, introduce new challenges for data mining. Additionally, due to their complex structure and their subjectivity to inaccuracies caused by perceptual effects, music data present challenges in knowledge representation as well.

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