  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.
