Acoustic event detection and localization with regression forests
Huy Phan, Marco Maaß, Radoslaw Mazur, Alfred Mertins · 2014
This paper proposes an approach for the efficient automatic joint detection and localization of single-channel acoustic events us-ing random forest regression. The audio signals are decom-posed into multiple densely overlapping superframes annotated with event class labels and their displacements to the temporal starting and ending points of the events. Using the displacement information, a multivariate random forest regression model is learned for each event category to map each superframe to con-tinuous estimates of onset and offset locations of the events. In addition, two classifiers are trained using random forest clas-sification to classify superframes of background and different event categories. On testing, based on the detection of category-specific superframes using the classifiers, the learned regressor provides the estimates of onset and offset locations in time of the corresponding event. While posing event detection and lo-calization as a regression problem is novel, the quantitative eval-uation on ITC-Irst database of highly variable acoustic events shows the efficiency and potential of the proposed approach. Index Terms: acoustic event detection, regression forest, ran-dom forest, superframe