  Machine learning from training data with a skewed distribution of examples
per class can lead to models that favor performance on common classes at the
expense of performance on rare ones. AudioSet has a very wide range of priors
over its 527 sound event classes. Classification performance on AudioSet is
usually evaluated by a simple average over per-class metrics, meaning that
performance on rare classes is equal in importance to the performance on common
ones. Several recent papers have used dataset balancing techniques to improve
performance on AudioSet. We find, however, that while balancing improves
performance on the public AudioSet evaluation data it simultaneously hurts
performance on an unpublished evaluation set collected under the same
conditions. By varying the degree of balancing, we show that its benefits are
fragile and depend on the evaluation set. We also do not find evidence
indicating that balancing improves rare class performance relative to common
classes. We therefore caution against blind application of balancing, as well
as against paying too much attention to small improvements on a public
evaluation set.
