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

CaR-FOREST: Joint Classification-Regression Decision Forests for Overlapping Audio Event Detection