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001 978-3-319-25931-4
003 DE-He213
005 20200421111654.0
007 cr nn 008mamaa
008 151027s2016 gw | s |||| 0|eng d
020 _a9783319259314
_9978-3-319-25931-4
024 7 _a10.1007/978-3-319-25931-4
_2doi
050 4 _aNX260
072 7 _aH
_2bicssc
072 7 _aUB
_2bicssc
072 7 _aCOM018000
_2bisacsh
072 7 _aART000000
_2bisacsh
082 0 4 _a004
_223
245 1 0 _aComputational Music Analysis
_h[electronic resource] /
_cedited by David Meredith.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 480 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aMusic Analysis by Computer Ontology and Epistemology -- The Harmonic Musical Surface and Two Novel Chord Representation Schemes -- Topological Structures in Computer-Aided Music Analysis -- Contextual Set-Class Analysis -- Computational Analysis of Musical Form -- Chord- and Note-Based Approaches to Voice Separation -- Analysing Symbolic Music with Probabilistic Grammars -- Interactive Melodic Analysis -- Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff's Generative Theory of Tonal Music -- An Algebraic Approach to Time-Span Reduction -- Automated Motivic Analysis An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces -- A Wavelet-Based Approach to Pattern Discovery in Melodies -- Analysing Music with Point-Set Compression Algorithms -- Composer Classification Models for Music-Theory Building -- Contrast Pattern Mining in Folk Music Analysis -- Pattern and Antipattern Discovery in Ethiopian Bagana Songs -- Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora -- Index.
520 _aThis 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.
650 0 _aComputer science.
650 0 _aMusic.
650 0 _aApplication software.
650 1 4 _aComputer Science.
650 2 4 _aComputer Appl. in Arts and Humanities.
650 2 4 _aMusic.
700 1 _aMeredith, David.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319259291
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-25931-4
912 _aZDB-2-SCS
942 _cEBK
999 _c54593
_d54593