Genetic Programming for Musical Sound Analysis
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Loughran:2012:EvoMUSART,
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author = "Roisin Loughran and Jacqueline Walker and
Michael O'Neill and James McDermott",
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title = "Genetic Programming for Musical Sound Analysis",
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booktitle = "Proceedings of the 1st International Conference on
Evolutionary and Biologically Inspired Music, Sound,
Art and Design, EvoMUSART 2012",
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year = "2012",
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month = "11-13 " # apr,
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editor = "Penousal Machado and Juan Romero and
Adrian Carballal",
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series = "LNCS",
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volume = "7247",
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publisher = "Springer Verlag",
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address = "Malaga, Spain",
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pages = "176--186",
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organisation = "EvoStar",
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isbn13 = "978-3-642-29141-8",
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DOI = "doi:10.1007/978-3-642-29142-5_16",
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keywords = "genetic algorithms, genetic programming, Musical
Information Retrieval, timbre",
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abstract = "This study uses Genetic Programming (GP) in developing
a classifier to distinguish between five musical
instruments. Using only simple arithmetic and Boolean
operators with 95 features as terminals, a program is
developed that can classify 300 unseen samples with an
accuracy of 94percent. The experiment is then run again
using only 14 of the most often chosen features.
Limiting the features in this way raised the best
classification to 94.3percent and the average accuracy
from 68.2percent to 75.67percent. This demonstrates
that not only can GP be used to create a classifier but
it can be used to determine the best features to choose
for accurate musical instrument classification, giving
an insight into timbre.",
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notes = "Part of \cite{Machado:2012:EvoMusArt_proc}
EvoMUSART'2012 held in conjunction with EuroGP2012,
EvoCOP2012, EvoBIO2012 and EvoApplications2012",
- }
Genetic Programming entries for
Roisin Loughran
Jacqueline Walker
Michael O'Neill
James McDermott
Citations