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Local binary pattern with random forest for acoustic scene classification

Abidin, S., Xia, X., Togneri, R. and Sohel, F. (2018) Local binary pattern with random forest for acoustic scene classification. In: IEEE International Conference on Multimedia and Expo (ICME) 2018, 23 - 27 July 2018, San Diego, CA

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This paper presents an approach for acoustic scene classification using the local binary pattern (LBP) and random forest (RF). The audio signal is converted to a Constant-Q transform (CQT) representation and LBP is used to extract the features from this time-frequency representation. The CQT representations are divided into a number of sub-bands to obtain more localized features relevant to the spectral information. We then use random forest to select the most important features for each band of extracted LBP features. For further performance enhancement, we use feature level fusion of LBP and HOG features. The proposed system has achieved an accuracy of 85% on the DCASE 2016 dataset.

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