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Spectro-Temporal analysis using local binary pattern variants for acoustic scene classification
Journal article   Peer reviewed

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

S. Abidin, R. Togneri and F. Sohel
IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol.26(11), pp.2112-2121
2018
DOI: https://doi.org/10.1109/TASLP.2018.2854861
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Abstract

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.

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23 Record Views
25 Times Cited - Web of Science

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Collaboration types
Domestic collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.174 Digital Signal Processing
4.174.152 Speech Recognition
Web Of Science research areas
Acoustics
Engineering, Electrical & Electronic
ESI research areas
Engineering
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