  Polyphonic events are the main error source of audio event detection (AED)
systems. In deep-learning context, the most common approach to deal with event
overlaps is to treat the AED task as a multi-label classification problem. By
doing this, we inherently consider multiple one-vs.-rest classification
problems, which are jointly solved by a single (i.e. shared) network. In this
work, to better handle polyphonic mixtures, we propose to frame the task as a
multi-class classification problem by considering each possible label
combination as one class. To circumvent the large number of arising classes due
to combinatorial explosion, we divide the event categories into multiple groups
and construct a multi-task problem in a divide-and-conquer fashion, where each
of the tasks is a multi-class classification problem. A network architecture is
then devised for multi-class multi-task modelling. The network is composed of a
backbone subnet and multiple task-specific subnets. The task-specific subnets
are designed to learn time-frequency and channel attention masks to extract
features for the task at hand from the common feature maps learned by the
backbone. Experiments on the TUT-SED-Synthetic-2016 with high degree of event
overlap show that the proposed approach results in more favorable performance
than the common multi-label approach.
