Random forest classification based acoustic event detection

    Research output: Chapter in Book/Conference paperConference paper

    8 Citations (Scopus)

    Abstract

    This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific random forest classifiers are then reversed back to the event boundary information. Evaluations on the UPC-TALP database which consists of highly variable acoustic events demonstrate the efficiency of the proposed approaches with improvements in detection error rate compared to the best baseline system.

    Original languageEnglish
    Title of host publicationProceedings of the IEEE International Conference on Multimedia and Expo 2017
    EditorsJörn Ostermann, Kenneth K.M. Lam
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages163-168
    Number of pages6
    ISBN (Print)9781509060672
    DOIs
    Publication statusPublished - 31 Aug 2017
    Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
    Duration: 10 Jul 201714 Jul 2017

    Conference

    Conference2017 IEEE International Conference on Multimedia and Expo, ICME 2017
    CountryHong Kong
    CityHong Kong
    Period10/07/1714/07/17

    Fingerprint

    Acoustics
    Error detection
    Labels
    Classifiers

    Cite this

    Xia, X., Togneri, R., Sohel, F., & Huang, D. (2017). Random forest classification based acoustic event detection. In J. Ostermann, & K. K. M. Lam (Eds.), Proceedings of the IEEE International Conference on Multimedia and Expo 2017 (pp. 163-168). [8019452] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICME.2017.8019452
    Xia, Xianjun ; Togneri, Roberto ; Sohel, Ferdous ; Huang, David. / Random forest classification based acoustic event detection. Proceedings of the IEEE International Conference on Multimedia and Expo 2017. editor / Jörn Ostermann ; Kenneth K.M. Lam. IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 163-168
    @inproceedings{ec4e5793e4f34e909df3af0e7838b24c,
    title = "Random forest classification based acoustic event detection",
    abstract = "This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific random forest classifiers are then reversed back to the event boundary information. Evaluations on the UPC-TALP database which consists of highly variable acoustic events demonstrate the efficiency of the proposed approaches with improvements in detection error rate compared to the best baseline system.",
    keywords = "Acoustic event detection, Discretization, Random forest, Regression via classification (RvC)",
    author = "Xianjun Xia and Roberto Togneri and Ferdous Sohel and David Huang",
    year = "2017",
    month = "8",
    day = "31",
    doi = "10.1109/ICME.2017.8019452",
    language = "English",
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    pages = "163--168",
    editor = "Ostermann, {J{\"o}rn } and Lam, {Kenneth K.M. }",
    booktitle = "Proceedings of the IEEE International Conference on Multimedia and Expo 2017",
    publisher = "IEEE, Institute of Electrical and Electronics Engineers",
    address = "United States",

    }

    Xia, X, Togneri, R, Sohel, F & Huang, D 2017, Random forest classification based acoustic event detection. in J Ostermann & KKM Lam (eds), Proceedings of the IEEE International Conference on Multimedia and Expo 2017., 8019452, IEEE, Institute of Electrical and Electronics Engineers, pp. 163-168, 2017 IEEE International Conference on Multimedia and Expo, ICME 2017, Hong Kong, Hong Kong, 10/07/17. https://doi.org/10.1109/ICME.2017.8019452

    Random forest classification based acoustic event detection. / Xia, Xianjun; Togneri, Roberto; Sohel, Ferdous; Huang, David.

    Proceedings of the IEEE International Conference on Multimedia and Expo 2017. ed. / Jörn Ostermann; Kenneth K.M. Lam. IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 163-168 8019452.

    Research output: Chapter in Book/Conference paperConference paper

    TY - GEN

    T1 - Random forest classification based acoustic event detection

    AU - Xia, Xianjun

    AU - Togneri, Roberto

    AU - Sohel, Ferdous

    AU - Huang, David

    PY - 2017/8/31

    Y1 - 2017/8/31

    N2 - This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific random forest classifiers are then reversed back to the event boundary information. Evaluations on the UPC-TALP database which consists of highly variable acoustic events demonstrate the efficiency of the proposed approaches with improvements in detection error rate compared to the best baseline system.

    AB - This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific random forest classifiers are then reversed back to the event boundary information. Evaluations on the UPC-TALP database which consists of highly variable acoustic events demonstrate the efficiency of the proposed approaches with improvements in detection error rate compared to the best baseline system.

    KW - Acoustic event detection

    KW - Discretization

    KW - Random forest

    KW - Regression via classification (RvC)

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    M3 - Conference paper

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    BT - Proceedings of the IEEE International Conference on Multimedia and Expo 2017

    A2 - Ostermann, Jörn

    A2 - Lam, Kenneth K.M.

    PB - IEEE, Institute of Electrical and Electronics Engineers

    ER -

    Xia X, Togneri R, Sohel F, Huang D. Random forest classification based acoustic event detection. In Ostermann J, Lam KKM, editors, Proceedings of the IEEE International Conference on Multimedia and Expo 2017. IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 163-168. 8019452 https://doi.org/10.1109/ICME.2017.8019452