  The study of label noise in sound event recognition has recently gained
attention with the advent of larger and noisier datasets. This work addresses
the problem of missing labels, one of the big weaknesses of large audio
datasets, and one of the most conspicuous issues for AudioSet. We propose a
simple and model-agnostic method based on a teacher-student framework with loss
masking to first identify the most critical missing label candidates, and then
ignore their contribution during the learning process. We find that a simple
optimisation of the training label set improves recognition performance without
additional computation. We discover that most of the improvement comes from
ignoring a critical tiny portion of the missing labels. We also show that the
damage done by missing labels is larger as the training set gets smaller, yet
it can still be observed even when training with massive amounts of audio. We
believe these insights can generalize to other large-scale datasets.
