The Impact of Label Noise on a Music Tagger
Katharina Prinz, Arthur Flexer, Gerhard Widmer · arXiv (Cornell University) · 2020
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.