  We propose a multi-label multi-task framework based on a convolutional
recurrent neural network to unify detection of isolated and overlapping audio
events. The framework leverages the power of convolutional recurrent neural
network architectures; convolutional layers learn effective features over which
higher recurrent layers perform sequential modelling. Furthermore, the output
layer is designed to handle arbitrary degrees of event overlap. At each time
step in the recurrent output sequence, an output triple is dedicated to each
event category of interest to jointly model event occurrence and temporal
boundaries. That is, the network jointly determines whether an event of this
category occurs, and when it occurs, by estimating onset and offset positions
at each recurrent time step. We then introduce three sequential losses for
network training: multi-label classification loss, distance estimation loss,
and confidence loss. We demonstrate good generalization on two datasets:
ITC-Irst for isolated audio event detection, and TUT-SED-Synthetic-2016 for
overlapping audio event detection.
