Music genre recognition with risk and rejection

Bob L. Sturm

Research output: Contribution to journalConference article in JournalResearchpeer-review

4 Citations (Scopus)
501 Downloads (Pure)

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.
Original languageEnglish
JournalInternational Conference on Multimedia and Expo
Publication statusPublished - 2013
Event2013 IEEE International Conference on Multimedia & Expo - San Jose, United States
Duration: 15 Jul 201319 Jul 2013

Conference

Conference2013 IEEE International Conference on Multimedia & Expo
CountryUnited States
CitySan Jose
Period15/07/201319/07/2013

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Experiments
Uncertainty

Cite this

@inproceedings{05b6c529bc894eef87943980223f70d8,
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 successwith respect to reducing false positives and negatives.",
author = "Sturm, {Bob L.}",
year = "2013",
language = "English",
journal = "International Conference on Multimedia and Expo",
publisher = "IEEE",

}

Music genre recognition with risk and rejection. / Sturm, Bob L.

In: International Conference on Multimedia and Expo, 2013.

Research output: Contribution to journalConference article in JournalResearchpeer-review

TY - GEN

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AU - Sturm, Bob L.

PY - 2013

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N2 - 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 successwith respect to reducing false positives and negatives.

AB - 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 successwith respect to reducing false positives and negatives.

M3 - Conference article in Journal

JO - International Conference on Multimedia and Expo

JF - International Conference on Multimedia and Expo

ER -