Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification

Seongkyu Mun, Suwon Shon, Wooil Kim, David K. Han, Hanseok Ko

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

    56 Citations (Scopus)

    Abstract

    Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for training Acoustic Scene Classification (ASC) models. By exploiting VOC DNN's ability of learning beyond its pre-trained environments, this paper proposes DNN based transfer learning for ASC. Effectiveness of the proposed method is demonstrated on the database of IEEE DCASE Challenge 2016 Task 1 and home surveillance environment via representative experiments. Its improved performance is verified by comparing it to prominent conventional methods.

    Original languageEnglish
    Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages796-800
    Number of pages5
    ISBN (Electronic)9781509041176
    DOIs
    Publication statusPublished - 2017 Jun 16
    Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
    Duration: 2017 Mar 52017 Mar 9

    Other

    Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
    Country/TerritoryUnited States
    CityNew Orleans
    Period17/3/517/3/9

    Keywords

    • acoustic scene classification
    • deep neural network
    • mid-level feature
    • Transfer learning

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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