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Symbolic music similarity using neuronal periodicity and dynamic programming
pp. 199-204
Abstract
We introduce NP-MUS, a symbolic music similarity algorithm tailored for polyphonic music with continuous representations of pitch and duration. The algorithm uses dynamic programming and a cost function that relies on a mathematical model of tonal fusion based on neuronal periodicity detection mechanisms. This paper reviews the general requirements of melodic similarity and offers a similarity method that better addresses contemporary and non-traditional music. We provide experiments based on monophonic and polyphonic excerpts inspired by spectral music and Iannis Xenakis.
Publication details
Published in:
Collins Tom, Meredith David, Volk Anja (2015) Mathematics and computation in music: 5th international conference, MCM 2015, London, UK, June 22-25, 2015. Dordrecht, Springer.
Pages: 199-204
DOI: 10.1007/978-3-319-20603-5_21
Full citation:
Valle Rafael, Freed Adrian (2015) „Symbolic music similarity using neuronal periodicity and dynamic programming“, In: T. Collins, D. Meredith & A. Volk (eds.), Mathematics and computation in music, Dordrecht, Springer, 199–204.