Random forest classification based acoustic event detection

    Research output: Chapter in Book/Conference paperConference paperpeer-review

    16 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
    Country/TerritoryHong Kong
    CityHong Kong
    Period10/07/1714/07/17

    Fingerprint

    Dive into the research topics of 'Random forest classification based acoustic event detection'. Together they form a unique fingerprint.

    Cite this