Paper Info
Title
Music genre recognition with risk and rejection
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.
Year
DOI
Venue
2013
10.1109/ICME.2013.6607607
Multimedia and Expo
Keywords
Field
DocType
Bayes methods,music,pattern classification,Bayesian classification,MGR system knowledge,minimum risk framework,music genre recognition,rejection,Bayesian classification,Music genre recognition,machine learning
Risk management framework,Naive Bayes classifier,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Machine learning,False positive paradox
Conference
ISSN
Citations 
PageRank 
1945-7871
3
0.40
References 
Authors
23
1
Name
Order
Citations
PageRank
Bob L. Sturm124129.88