  Acoustic events are sounds with well-defined spectro-temporal characteristics
which can be associated with the physical objects generating them. Acoustic
scenes are collections of such acoustic events in no specific temporal order.
Given this natural linkage between events and scenes, a common belief is that
the ability to classify events must help in the classification of scenes. This
has led to several efforts attempting to do well on Acoustic Event Tagging
(AET) and Acoustic Scene Classification (ASC) using a multi-task network.
However, in these efforts, improvement in one task does not guarantee an
improvement in the other, suggesting a tension between ASC and AET. It is
unclear if improvements in AET translates to improvements in ASC. We explore
this conundrum through an extensive empirical study and show that under certain
conditions, using AET as an auxiliary task in the multi-task network
consistently improves ASC performance. Additionally, ASC performance further
improves with the AET data-set size and is not sensitive to the choice of
events or the number of events in the AET data-set. We conclude that this
improvement in ASC performance comes from the regularization effect of using
AET and not from the network's improved ability to discern between acoustic
events.
