Enhanced LBP texture features from time frequency representations for acoustic scene classification

Shamsiah Abidin; Roberto Togneri; Ferdous Sohel

doi:10.1109/ICASSP.2017.7952231

Enhanced LBP texture features from time frequency representations for acoustic scene classification

Shamsiah Abidin, Roberto Togneri, Ferdous Sohel

School of Engineering

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

14 Citations (Scopus)

Abstract

This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relevant local features which better characterize the audio TFRs. To further improve the classification performance, we perform feature and score level fusion of the proposed LBP (with zoning) with histogram of gradients (HOG) of the TFR images. Our technique demonstrates an improved performance by achieving a classification accuracy of 95.2% using a fusion of time-frequency derived features.

Original language	English
Title of host publication	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings
Editors	Magdy A. Bayoumi
Place of Publication	New Orleans
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	626-630
Number of pages	5
ISBN (Electronic)	9781509041176
DOIs	https://doi.org/10.1109/ICASSP.2017.7952231
Publication status	Published - 19 Jun 2017
Event	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: 5 Mar 2017 → 9 Mar 2017

Conference

Conference	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Abbreviated title	ICASSP 2017
Country/Territory	United States
City	New Orleans
Period	5/03/17 → 9/03/17

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10.1109/ICASSP.2017.7952231

Cite this

Abidin, S., Togneri, R., & Sohel, F. (2017). Enhanced LBP texture features from time frequency representations for acoustic scene classification. In M. A. Bayoumi (Ed.), 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings (pp. 626-630). Article 7952231 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2017.7952231

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title = "Enhanced LBP texture features from time frequency representations for acoustic scene classification",

abstract = "This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relevant local features which better characterize the audio TFRs. To further improve the classification performance, we perform feature and score level fusion of the proposed LBP (with zoning) with histogram of gradients (HOG) of the TFR images. Our technique demonstrates an improved performance by achieving a classification accuracy of 95.2% using a fusion of time-frequency derived features.",

keywords = "acoustic scene, feature extraction, fusion, local binary patterns, time-frequency analysis",

author = "Shamsiah Abidin and Roberto Togneri and Ferdous Sohel",

year = "2017",

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doi = "10.1109/ICASSP.2017.7952231",

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booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",

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Abidin, S, Togneri, R & Sohel, F 2017, Enhanced LBP texture features from time frequency representations for acoustic scene classification. in MA Bayoumi (ed.), 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings., 7952231, IEEE, Institute of Electrical and Electronics Engineers, New Orleans, pp. 626-630, 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, New Orleans, United States, 5/03/17. https://doi.org/10.1109/ICASSP.2017.7952231

Enhanced LBP texture features from time frequency representations for acoustic scene classification. / Abidin, Shamsiah; Togneri, Roberto; Sohel, Ferdous.
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings. ed. / Magdy A. Bayoumi. New Orleans: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 626-630 7952231.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - Enhanced LBP texture features from time frequency representations for acoustic scene classification

AU - Abidin, Shamsiah

AU - Togneri, Roberto

AU - Sohel, Ferdous

PY - 2017/6/19

Y1 - 2017/6/19

N2 - This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relevant local features which better characterize the audio TFRs. To further improve the classification performance, we perform feature and score level fusion of the proposed LBP (with zoning) with histogram of gradients (HOG) of the TFR images. Our technique demonstrates an improved performance by achieving a classification accuracy of 95.2% using a fusion of time-frequency derived features.

AB - This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relevant local features which better characterize the audio TFRs. To further improve the classification performance, we perform feature and score level fusion of the proposed LBP (with zoning) with histogram of gradients (HOG) of the TFR images. Our technique demonstrates an improved performance by achieving a classification accuracy of 95.2% using a fusion of time-frequency derived features.

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KW - feature extraction

KW - fusion

KW - local binary patterns

KW - time-frequency analysis

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BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings

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PB - IEEE, Institute of Electrical and Electronics Engineers

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T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

Y2 - 5 March 2017 through 9 March 2017

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Abidin S, Togneri R, Sohel F. Enhanced LBP texture features from time frequency representations for acoustic scene classification. In Bayoumi MA, editor, 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings. New Orleans: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 626-630. 7952231 doi: 10.1109/ICASSP.2017.7952231

Enhanced LBP texture features from time frequency representations for acoustic scene classification

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