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Abstract
We explore risk and rejection for music genre recognition (MGR)
within the minimum risk framework of Bayesian classification.
In this way, we attempt to give an MGR system knowledge that
some misclassifications are worse than others,
and that deferring classification to an expert may be a better option
than forcing a label under high uncertainty.
Our experiments show this approach to have some success
with respect to reducing false positives and negatives.
within the minimum risk framework of Bayesian classification.
In this way, we attempt to give an MGR system knowledge that
some misclassifications are worse than others,
and that deferring classification to an expert may be a better option
than forcing a label under high uncertainty.
Our experiments show this approach to have some success
with respect to reducing false positives and negatives.
| Original language | English |
|---|---|
| Journal | International Conference on Multimedia and Expo |
| Publication status | Published - 2013 |
| Event | 2013 IEEE International Conference on Multimedia & Expo - San Jose, United States Duration: 15 Jul 2013 → 19 Jul 2013 |
Conference
| Conference | 2013 IEEE International Conference on Multimedia & Expo |
|---|---|
| Country/Territory | United States |
| City | San Jose |
| Period | 15/07/2013 → 19/07/2013 |
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Greedy Sparse Approximation and the Automatic Description of Audio and Music Data
Sturm, B. L.
Technology and Production Independent Postdoc Center for Independent Research
01/01/2012 → …
Project: Research
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CoSound
Christensen, M. G., Tan, Z., Jensen, S. H. & Sturm, B. L.
01/01/2012 → 31/12/2015
Project: Research