  The Detection and Classification of Acoustic Scenes and Events Challenge Task
4 aims to advance sound event detection (SED) systems in domestic environments
by leveraging training data with different supervision uncertainty.
Participants are challenged in exploring how to best use training data from
different domains and with varying annotation granularity (strong/weak temporal
resolution, soft/hard labels), to obtain a robust SED system that can
generalize across different scenarios. Crucially, annotation across available
training datasets can be inconsistent and hence sound labels of one dataset may
be present but not annotated in the other one and vice-versa. As such, systems
will have to cope with potentially missing target labels during training.
Moreover, as an additional novelty, systems will also be evaluated on labels
with different granularity in order to assess their robustness for different
applications. To lower the entry barrier for participants, we developed an
updated baseline system with several caveats to address these aforementioned
problems. Results with our baseline system indicate that this research
direction is promising and is possible to obtain a stronger SED system by using
diverse domain training data with missing labels compared to training a SED
system for each domain separately.
