@INBOOK{KarydisNM2006b,
  AUTHOR =       {Ioannis Karydis and Alexandros Nanopoulos and Yannis Manolopoulos},
  TITLE =        {Mining in Music Databases},
  CHAPTER =      {Processing and Managing Complex Data for Decision Support},
  pages =        {340--374},
  PUBLISHER =    {IDEA Group},
  YEAR =         {2006},
  abstract =     {The continuously increasing spread of music on the Internet as
                    well as in digital music libraries expands the already immense
                    interest of the public and the entertainment industry in music
                    databases. An account of research and development issues
                    concerning a variety of digital music libraries can be found
                    in~\cite{BBD01}. As the number of music databases grows
                    rapidly, so does their size, complexity, usage and accordingly
                    the need to provide flexible and efficient search, retrieval
                    and knowledge discovery techniques.

                    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 (more than one
                    events occurring at any single moment) 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.

                    This chapter provides a broad survey of music data mining,
                    including clustering, classification and pattern discovery in
                    music (the presented research also includes some recent
                    research results contributed by the authors). The data studied
                    in the tutorial is mainly symbolic encodings of musical scores,
                    although digital audio (acoustic data) is also addressed.
                    Throughout the chapter, practical applications of music data
                    mining is presented.

                    The theory and methods presented herein are relevant to both
                    data mining researchers interested in applying already known
                    techniques to music and those with a desire for familiarisation
                    with the a new area. Music basic rudiments are helpful for
                    readers but not prerequisite.},
}