Computational Music Analysis (43 results)

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Taschenbuch. Condition: Neu. Computational Music Analysis | David Meredith | Taschenbuch | xv | Englisch | 2016 | Springer | EAN 9783319387444 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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'sGenerative 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 retrievaland related disciplines. It also provides a state-of-the-art reference for practitioners in the music technology industry.…

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Music is built from sound, ultimately resulting from an elaborate interaction between the sound-generating properties of physical objects (i.e. music instruments) and the sound perception abilities of the human auditory system. Humans, even without any kind of formal music training, are typically able to extract, almost unconsciously, a great amount of relevant information from a musical signal (e.g. the beat and main melody of a musical piece, or the sound sources playing in a complex musical arrangement). In order to do so, the human auditory system uses a variety of cues for perceptual grouping such as similarity, proximity, harmonicity, common fate, among others. This book proposes a flexible and extensible Computational Auditory Scene Analysis (CASA) framework for modeling perceptual grouping in music listening. Implemented using the open source sound processing framework Marsyas, this work should be specially interesting to researchers in Music Information Retrieval (MIR) and CASA fields, or anyone developing software to perform automatic analysis and processing of sound and music signals.…

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