Random forest regression based acoustic event detection with bottleneck features

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    9 Citations (Scopus)

    Abstract

    This paper deals with random forest regression based acoustic event detection (AED) by combining acoustic features with bottleneck features (BN). The bottleneck features have a good reputation of being inherently discriminative in acoustic signal processing. To deal with the unstructured and complex real-world acoustic events, an acoustic event detection system is constructed using bottleneck features combined with acoustic features. Evaluations were carried out on the UPC-TALP and ITC-Irst databases which consist of highly variable acoustic events. Experimental results demonstrate the usefulness of the low-dimensional and discriminative bottleneck features with relative 5.33% and 5.51% decreases in error rates respectively.

    Original languageEnglish
    Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages157-162
    Number of pages6
    ISBN (Electronic)9781509060672
    DOIs
    Publication statusPublished - 28 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

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