Projects per year
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.
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 language | English |
|---|---|
| Journal | I E E E Signal Processing Letters |
| Volume | 20 |
| Issue number | 11 |
| Pages (from-to) | 1050-1053 |
| Number of pages | 4 |
| ISSN | 1070-9908 |
| DOIs | |
| Publication status | Published - Nov 2013 |
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Dive into the research topics of 'Revisiting Inter-Genre Similarity'. Together they form a unique fingerprint.Projects
- 2 Finished
-
Greedy Sparse Approximation and the Automatic Description of Audio and Music Data
Sturm, B. L. (Project Participant)
Technology and Production Independent Postdoc Center for Independent Research
01/01/2012 → 01/01/2015
Project: Research
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CoSound
Christensen, M. G. (Project Participant), Tan, Z.-H. (Project Participant), Jensen, S. H. (Project Participant) & Sturm, B. L. (Project Participant)
01/01/2012 → 31/12/2015
Project: Research