  The attention mechanism has been widely adopted in acoustic scene
classification. However, we find that during the process of attention
exclusively emphasizing information, it tends to excessively discard
information although improving the performance. We propose a mechanism referred
to as the attentive max feature map which combines two effective techniques,
attention and max feature map, to further elaborate the attention mechanism and
mitigate the abovementioned phenomenon. Furthermore, we explore various joint
learning methods that utilize additional labels originally generated for
subtask B (3-classes) on top of existing labels for subtask A (10-classes) of
the DCASE2020 challenge. We expect that using two kinds of labels
simultaneously would be helpful because the labels of the two subtasks differ
in their degree of abstraction. Applying two proposed techniques, our proposed
system achieves state-of-the-art performance among single systems on subtask A.
In addition, because the model has a complexity comparable to subtask B's
requirement, it shows the possibility of developing a system that fulfills the
requirements of both subtasks; generalization on multiple devices and
low-complexity.
