The Impact of Label Noise on a Music Tagger

Katharina Prinz; Arthur Flexer; Gerhard Widmer

The Impact of Label Noise on a Music Tagger

Audio and Speech Processing 2020-08-17 v1 Machine Learning Sound

Authors: Katharina Prinz , Arthur Flexer , Gerhard Widmer

Abstract

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.

Keywords

label noise music tagging audio tagging noisy labels curated data

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@article{arxiv.2008.06273,
  title  = {The Impact of Label Noise on a Music Tagger},
  author = {Katharina Prinz and Arthur Flexer and Gerhard Widmer},
  journal= {arXiv preprint arXiv:2008.06273},
  year   = {2020}
}

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In Proceedings of the 13th International Workshop on Machine Learning and Music, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

The Impact of Label Noise on a Music Tagger

Abstract

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