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Random forest classification based acoustic event detection

Xia, X., Togneri, R., Sohel, F. and Huang, D. (2017) Random forest classification based acoustic event detection. In: IEEE International Conference on Multimedia and Expo (ICME) 2017, 10 - 14 July 2017, Hong Kong, China

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This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific random forest classifiers are then reversed back to the event boundary information. Evaluations on the UPC-TALP database which consists of highly variable acoustic events demonstrate the efficiency of the proposed approaches with improvements in detection error rate compared to the best baseline system.

Item Type: Conference Paper
Murdoch Affiliation(s): School of Engineering and Information Technology
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