A Wavelet-Based Approach to Pattern Discovery in Melodies. Velarde, G., Meredith, D., & Weyde, T. In Computational Music Analysis, 12, pages 303–333. Springer International Publishing, Cham, 2016.
A Wavelet-Based Approach to Pattern Discovery in Melodies [link]Paper  doi  abstract   bibtex   
This book provides an in-depth introduction and overview of current research in computational music analysis. Its seventeen chapters, written by leading researchers, collectively represent the diversity as well as the technical and philosophical sophistication of the work being done today in this intensely interdisciplinary field. A broad range of approaches are presented, employing techniques originating in disciplines such as linguistics, information theory, information retrieval, pattern recognition, machine learning, topology, algebra and signal processing. Many of the methods described draw on well-established theories in music theory and analysis, such as Forte's pitch-class set theory, Schenkerian analysis, the methods of semiotic analysis developed by Ruwet and Nattiez, and Lerdahl and Jackendoff's Generative Theory of Tonal Music. The book is divided into six parts, covering methodological issues, harmonic and pitch-class set analysis, form and voice-separation, grammars and hierarchical reduction, motivic analysis and pattern discovery and, finally, classification and the discovery of distinctive patterns. As a detailed and up-to-date picture of current research in computational music analysis, the book provides an invaluable resource for researchers, teachers and students in music theory and analysis, computer science, music information retrieval and related disciplines. It also provides a state-of-the-art reference for practitioners in the music technology industry.
@incollection{velarde.ea2016-wavelet,
  abstract =     {This book provides an in-depth introduction and
                  overview of current research in computational music
                  analysis. Its seventeen chapters, written by leading
                  researchers, collectively represent the diversity as
                  well as the technical and philosophical
                  sophistication of the work being done today in this
                  intensely interdisciplinary field. A broad range of
                  approaches are presented, employing techniques
                  originating in disciplines such as linguistics,
                  information theory, information retrieval, pattern
                  recognition, machine learning, topology, algebra and
                  signal processing. Many of the methods described
                  draw on well-established theories in music theory
                  and analysis, such as Forte's pitch-class set
                  theory, Schenkerian analysis, the methods of
                  semiotic analysis developed by Ruwet and Nattiez,
                  and Lerdahl and Jackendoff's Generative Theory of
                  Tonal Music. The book is divided into six parts,
                  covering methodological issues, harmonic and
                  pitch-class set analysis, form and voice-separation,
                  grammars and hierarchical reduction, motivic
                  analysis and pattern discovery and, finally,
                  classification and the discovery of distinctive
                  patterns. As a detailed and up-to-date picture of
                  current research in computational music analysis,
                  the book provides an invaluable resource for
                  researchers, teachers and students in music theory
                  and analysis, computer science, music information
                  retrieval and related disciplines. It also provides
                  a state-of-the-art reference for practitioners in
                  the music technology industry.},
  address =      {Cham},
  author =       {Velarde, Gissel and Meredith, David and Weyde,
                  Tillman},
  booktitle =    {Computational Music Analysis},
  chapter =      12,
  doi =          {10.1007/978-3-319-25931-4_12},
  editor =       {Meredith, David},
  isbn =         9783319259314,
  issn =         {0098-7484},
  keywords =     {music analysis with computers},
  mendeley-tags ={music analysis with computers},
  pages =        {303--333},
  pmid =         1689,
  publisher =    {Springer International Publishing},
  title =        {{A Wavelet-Based Approach to Pattern Discovery in
                  Melodies}},
  url =
                  {http://link.springer.com/10.1007/978-3-319-25931-4{\_}12},
  year =         2016
}

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