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Spectro-Temporal analysis using local binary pattern variants for acoustic scene classification

Abidin, S., Togneri, R. and Sohel, F. (2018) Spectro-Temporal analysis using local binary pattern variants for acoustic scene classification. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26 (11). pp. 2112-2121.

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In this paper we present an approach for acoustic scene classification, which aggregates spectral and temporal features. We do this by proposing the first use of the variable-Q transform (VQT) to generate the time-frequency representation for acoustic scene classification. The VQT provides finer control over the resolution compared to the constant-Q transform (CQT) or STFT and can be tuned to better capture acoustic scene information. We then adopt a variant of the local binary pattern (LBP), the Adjacent Evaluation Completed LBP (AECLBP), which is better suited to extracting features from acoustic time-frequency images. Our results yield a 5.2% improvement on the DCASE 2016 dataset compared to the application of standard CQT with LBP. Fusing our proposed AECLBP with HOG features we achieve a classification accuracy of 85.5% which outperforms one of the top performing systems.

Item Type: Journal Article
Murdoch Affiliation(s): School of Engineering and Information Technology
Publisher: IEEE
Copyright: © 2018 IEEE
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