Computational Music Analysis by David Meredith

By David Meredith

This e-book presents an in-depth creation and review of present learn in computational song research. Its seventeen chapters, written by way of top researchers, jointly symbolize the range in addition to the technical and philosophical sophistication of the paintings being performed at the present time during this intensely interdisciplinary box. A extensive variety of ways are awarded, making use of concepts originating in disciplines corresponding to linguistics, info concept, details retrieval, development reputation, computer studying, topology, algebra and sign processing. the various equipment defined draw on well-established theories in track concept and research, reminiscent of Forte's pitch-class set concept, Schenkerian research, the tools of semiotic research built through Ruwet and Nattiez, and Lerdahl and Jackendoff's Generative thought of Tonal tune. The e-book is split into six components, overlaying methodological concerns, harmonic and pitch-class set research, shape and voice-separation, grammars and hierarchical aid, motivic research and trend discovery and, ultimately, type and the invention of specified styles. As a close and updated photo of present study in computational track research, the e-book presents a useful source for researchers, academics and scholars in track concept and research, machine technological know-how, track info retrieval and comparable disciplines. It additionally presents a state of the art reference for practitioners within the tune expertise undefined.

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And Jackendoff, R. (1983). A Generative Theory of Tonal Music. MIT Press. Levinson, J. (1980). What a musical work is. Journal of Philosophy, 77(1):5–28. Marsden, A. (2010). Schenkerian analysis by computer: A proof of concept. Journal of New Music Research, 39(3):269–289. Mawer, D. (1999). Bridging the divide: embedding voice-leading analysis in string pedagogy and performance. British Journal of Music Education, 16(2):179–195. , and De Bie, T. (2011). Using online chord databases to enhance chord recognition.

6 Conclusions Music analysis is not a monolithic enterprise: different analysts do different things on different bases. Computational analysis therefore should also be multifarious. Most importantly, it should not and cannot be simply a machine reflection of the human activity. I conclude here with some reflections and recommendations about the ways in which computational analysis might profitably be used, and some recommendations on building software tools for music analysis. 1 Value of Computational Analysis Computational music analysis needs to carve out a place for itself where it is not simply mimicry of human analysis, but a place which is not so distant from the human activity to prevent useful communication with musicians.

2015). Music analysis and point-set compression. Journal of New Music Research, 44(3). In press. -J. (1982). 5’: A study in semiological music analysis. Music Analysis, 1(3):243–340. -J. (1990). Music and Discourse: Towards a Semiology of Music. Princeton University Press. Pauwels, J. -P. (2014). Combining musicological knowledge about chords and keys in a simultaneous chord and local key estimation system. Journal of New Music Research, 43(3):318–330. Pearce, M. and Wiggins, G. (2012). Auditory expectation: The information dynamics of music perception and cognition.

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