Conference paper
Random forest regression based acoustic event detection with bottleneck features
2017 IEEE International Conference on Multimedia and Expo (ICME)
IEEE International Conference on Multimedia and Expo (ICME) 2017 (Hong Kong, China, 10/07/2017–14/07/2017)
2017
Abstract
This paper deals with random forest regression based acoustic event detection (AED) by combining acoustic features with bottleneck features (BN). The bottleneck features have a good reputation of being inherently discriminative in acoustic signal processing. To deal with the unstructured and complex real-world acoustic events, an acoustic event detection system is constructed using bottleneck features combined with acoustic features. Evaluations were carried out on the UPC-TALP and ITC-Irst databases which consist of highly variable acoustic events. Experimental results demonstrate the usefulness of the low-dimensional and discriminative bottleneck features with relative 5.33% and 5.51% decreases in error rates respectively.
Details
- Title
- Random forest regression based acoustic event detection with bottleneck features
- Authors/Creators
- X. Xia (Author/Creator) - The University of Western AustraliaR. Togneri (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - Murdoch UniversityD. Huang (Author/Creator) - The University of Western Australia
- Publication Details
- 2017 IEEE International Conference on Multimedia and Expo (ICME)
- Conference
- IEEE International Conference on Multimedia and Expo (ICME) 2017 (Hong Kong, China, 10/07/2017–14/07/2017)
- Identifiers
- 991005543710507891
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Conference paper
Metrics
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