A MULTI-CHANNEL FUSION FRAMEWORK FOR AUDIO EVENT DETECTION

We propose in this paper a simple, yet efficient multi-channel fusion framework for joint acoustic event detection and classification. The joint problem on individual channels is posed as a regression problem to estimate event onset and offset positions. As an intermediate result, we also obtain the posterior probabilities which measure the confidence that event onsets and offsets are present at a temporal position. It facilitates the fusion problem by accumulating the posterior probabilities of different channels. The detection hypotheses are then determined based on the summed posterior probabilities. While the proposed fusion framework appears to be simple and natural, it significantly outperforms all the single-channel baseline systems on the ITC-Irst database. We also show that adding channels one by one into the fusion system yields performance improvements, and the performance of the fusion system is always better than those of the individual-channel counterparts.