• Corpus ID: 208652577

WEAK MULTI-LABEL AUDIO-TAGGING WITH CLASS NOISE

@inproceedings{Prinz2019WEAKMA,
  title={WEAK MULTI-LABEL AUDIO-TAGGING WITH CLASS NOISE},
  author={Katharina Prinz and Arthur Flexer},
  year={2019}
}
The necessity of annotated data for supervised learning often contrasts with the cost of obtaining reliable ground-truth in a manual fashion. Automated methods, on the other hand, simplify the annotation process and result in greater quantities of data with possibly noisy labels. Task 2 of the DCASE2019 Challenge, titled "Audio tagging with noisy labels and minimal supervision", tried to answer the question whether such data can be incorporated into an audio-tagging learning process in a… 
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The Impact of Label Noise on a Music Tagger
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It is shown that carefully annotated labels result in highest figures of merit, but even high amounts of noisy labels contain enough information for successful learning.

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