Neuro-genetic programming for multigenre classification of music content
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- @Article{CAMPOBELLO2020106488,
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author = "Giuseppe Campobello and Daniele Dell'Aquila and
Marco Russo and Antonino Segreto",
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title = "Neuro-genetic programming for multigenre
classification of music content",
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journal = "Applied Soft Computing",
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year = "2020",
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volume = "94",
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pages = "106488",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Artificial
neural networks, ANN, Music genre recognition,
Multi-label classifiers, Fuzzy classification",
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ISSN = "1568-4946",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494620304270",
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DOI = "doi:10.1016/j.asoc.2020.106488",
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abstract = "A machine learning approach based on hybridization of
genetic programming and neural networks is used to
derive mathematical models for music genre
classification. We design three multi-label classifiers
with different trade-offs between complexity and
accuracy, which are able to identify the degree of
belonging of music content to ten different music
genres. Our approach is innovative as it entirely
relies on simple analytical functions and a reduced
number of features. Resulting classifiers have an
extremely low computational complexity and are suitable
to be easily integrated in low-cost embedded systems
for real-time applications. The GTZAN dataset is used
for model training and to evaluate the accuracy of the
proposed classifiers. Despite of the reduced number of
features used in our approach, the accuracy of our
models is found to be similar to that of more complex
music genre classification tools previously published
in the literature.",
- }
Genetic Programming entries for
Giuseppe Campobello
Daniele Dell'Aquila
Marco Russo
Antonino Segreto
Citations