Data-driven audio feature selection for audio quality recognition in broadcast news

Theodoros Theodorou, Iosif Mporas, Ilyas Potamitis, Nikos Fakotakis

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In1 this paper, we describe automatic audio quality recognition architecture for radio broadcast news based on audio feature selection, using the discrimination ability of the audio descriptors as a criterion of selection. Specifically, we labeled streams of broadcast news transmissions according to their audio quality based on the human auditory perception. Parameterization algorithms extract a large set of audio descriptors and an algorithm of data-driven criteria rank the descriptors’ relevance. After that, the feature subsets fed machine learning algorithms for classification. This methodology showed that the k-nearest neighbor classifier provides significantly good results, considering the achieved accuracy. Moreover, the experimental framework verifies the assumption that discarding irrelevant audio descriptors before the classification stage works in favor to the overall identification performance.

    Original languageEnglish
    Title of host publicationProceedings - 10th Hellenic Conference on Artificial Intelligence, SETN 2018
    PublisherACM Press
    ISBN (Electronic)9781450364331
    DOIs
    Publication statusPublished - 9 Jul 2018
    Event10th Hellenic Conference on Artificial Intelligence, SETN 2018 - Patras, Greece
    Duration: 9 Jul 201812 Jul 2018

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference10th Hellenic Conference on Artificial Intelligence, SETN 2018
    Country/TerritoryGreece
    CityPatras
    Period9/07/1812/07/18

    Keywords

    • Audio feature selection
    • Automatic audio quality recognition
    • Broadcast news

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