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.
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