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Acoustic Event Detection and Localization with Regression Forests

Phan, Huy, Maass, Marco, Mazur, Radoslaw, Mertins, Alfred (2014) Acoustic Event Detection and Localization with Regression Forests. In: 15th Annual Conference of the International Speech Communication Association (INTERSPEECH 2014). . pp. 2524-2528. ISCA (KAR id:72695)


This paper proposes an approach for the efficient automatic joint detection and localization of single-channel acoustic events using random forest regression. The audio signals are decomposed into multiple densely overlapping {\em 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 continuous estimates of onset and offset locations of the events. In addition, two classifiers are trained using random forest classification 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 localization as a regression problem is novel, the quantitative evaluation on ITC-Irst database of highly variable acoustic events shows the efficiency and potential of the proposed approach.

Item Type: Conference or workshop item (Proceeding)
Uncontrolled keywords: acoustic event detection, regression forest, random forest, superframe
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Huy Phan
Date Deposited: 25 Feb 2019 17:22 UTC
Last Modified: 16 Feb 2021 14:02 UTC
Resource URI: (The current URI for this page, for reference purposes)

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