  Current audio classification models have small class vocabularies relative to
the large number of sound event classes of interest in the real world. Thus,
they provide a limited view of the world that may miss important yet unexpected
or unknown sound events. To address this issue, open-set audio classification
techniques have been developed to detect sound events from unknown classes.
Although these methods have been applied to a multi-class context in audio,
such as sound scene classification, they have yet to be investigated for
polyphonic audio in which sound events overlap, requiring the use of
multi-label models. In this study, we establish the problem of multi-label
open-set audio classification by creating a dataset with varying unknown class
distributions and evaluating baseline approaches built upon existing
techniques.
