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Random forest classification based acoustic event detection utilizing contextual-information and bottleneck features

Xia, X., Togneri, R., Sohel, F. and Huang, D. (2018) Random forest classification based acoustic event detection utilizing contextual-information and bottleneck features. Pattern Recognition, 81 . pp. 1-13.

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Link to Published Version: https://doi.org/10.1016/j.patcog.2018.03.025
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Abstract

The variety of event categories and event boundary information have resulted in limited success for acoustic event detection systems. To deal with this, we propose to utilize the long contextual information, low-dimensional discriminant global bottleneck features and category-specific bottleneck features. By concatenating several adjacent frames together, the use of contextual information makes it easier to cope with acoustic signals with long duration. Global and category-specific bottleneck features can extract the prior knowledge of the event category and boundary, which is ideally matched by the task of an event detection system. Evaluations on the UPC-TALP and ITC-IRST databases of highly variable acoustic events demonstrate the effectiveness of the proposed approaches by achieving a 5.30% and 4.44% absolute error rate improvement respectively compared to the state of art technique.

Publication Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Elsevier
Copyright: © 2018 Elsevier Ltd
URI: http://researchrepository.murdoch.edu.au/id/eprint/40583
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