Revisiting Inter-Genre Similarity

Bob L. Sturm, Fabien Gouyon

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)
309 Downloads (Pure)

Abstract

We revisit the idea of ``inter-genre similarity'' (IGS)
for machine learning in general,
and music genre recognition in particular.
We show analytically that the probability of error for IGS
is higher than naive Bayes classification with zero-one loss (NB).
We show empirically that IGS does not perform well,
even for data that satisfies all its assumptions.
Original languageEnglish
JournalI E E E Signal Processing Letters
Volume20
Issue number11
Pages (from-to)1050-1053
Number of pages4
ISSN1070-9908
DOIs
Publication statusPublished - Nov 2013

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Learning systems
Naive Bayes
Music
Machine Learning
Zero
Similarity

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Sturm, Bob L. ; Gouyon, Fabien. / Revisiting Inter-Genre Similarity. In: I E E E Signal Processing Letters. 2013 ; Vol. 20, No. 11. pp. 1050-1053.
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abstract = "We revisit the idea of ``inter-genre similarity'' (IGS)for machine learning in general,and music genre recognition in particular.We show analytically that the probability of error for IGSis higher than naive Bayes classification with zero-one loss (NB).We show empirically that IGS does not perform well,even for data that satisfies all its assumptions.",
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Revisiting Inter-Genre Similarity. / Sturm, Bob L.; Gouyon, Fabien.

In: I E E E Signal Processing Letters, Vol. 20, No. 11, 11.2013, p. 1050-1053.

Research output: Contribution to journalJournal articleResearchpeer-review

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AB - We revisit the idea of ``inter-genre similarity'' (IGS)for machine learning in general,and music genre recognition in particular.We show analytically that the probability of error for IGSis higher than naive Bayes classification with zero-one loss (NB).We show empirically that IGS does not perform well,even for data that satisfies all its assumptions.

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