  Polyphonic Sound Event Detection (SED) in real-world recordings is a
challenging task because of the dynamic polyphony level, intensity, and
duration of sound events. Current polyphonic SED systems fail to model the
temporal structure of sound events explicitly and instead attempt to look at
which sound events are present at each audio frame. Consequently, the
event-wise detection performance is much lower than the segment-wise detection
performance. In this work, we propose a joint model approach to improve the
temporal localization of sound events using a multi-task learning setup. The
first task predicts which sound events are present at each time frame; we call
this branch 'Sound Event Detection (SED) model', while the second task predicts
if a sound event is present or not at each frame; we call this branch 'Sound
Activity Detection (SAD) model'. We verify the proposed joint model by
comparing it with a separate implementation of both tasks aggregated together
from individual task predictions. Our experiments on the URBAN-SED dataset show
that the proposed joint model can alleviate False Positive (FP) and False
Negative (FN) errors and improve both the segment-wise and the event-wise
metrics.
