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The Impact of Label Noise on a Music Tagger

We explore how much can be learned from noisy labels in audio music tagging. Our experiments show that carefully annotated labels result in highest figures of merit, but even high amounts of noisy labels contain enough information for successful learning. Artificial corruption of curated data allows us to quantize this contribution of noisy labels.

preprint2020arXivOpen access
Katharina PrinzArthur FlexerGerhard Widmer
eess.ASMachine LearningSound
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Open access3 authors3 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Katharina PrinzArthur FlexerGerhard Widmer

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