  Audio classification has seen great progress with the increasing availability
of large-scale datasets. These large datasets, however, are often only
partially labeled as collecting full annotations is a tedious and expensive
process. This paper presents two semi-supervised methods capable of learning
with missing labels and evaluates them on two publicly available, partially
labeled datasets. The first method relies on label enhancement by a two-stage
teacher-student learning process, while the second method utilizes the mean
teacher semi-supervised learning algorithm. Our results demonstrate the impact
of improperly handling missing labels and compare the benefits of using
different strategies leveraging data with few labels. Methods capable of
learning with partially labeled data have the potential to improve models for
audio classification by utilizing even larger amounts of data without the need
for complete annotations.
