  This report describes our submissions to Task2 and Task3 of the DCASE 2016
challenge. The systems aim at dealing with the detection of overlapping audio
events in continuous streams, where the detectors are based on random decision
forests. The proposed forests are jointly trained for classification and
regression simultaneously. Initially, the training is classification-oriented
to encourage the trees to select discriminative features from overlapping
mixtures to separate positive audio segments from the negative ones. The
regression phase is then carried out to let the positive audio segments vote
for the event onsets and offsets, and therefore model the temporal structure of
audio events. One random decision forest is specifically trained for each event
category of interest. Experimental results on the development data show that
our systems significantly outperform the baseline on the Task2 evaluation while
they are inferior to the baseline in the Task3 evaluation.
