Deep neural network bottleneck features for acoustic event recognition

Seongkyu Mun, Suwon Shon, Wooil Kim, Hanseok Ko

    Research output: Contribution to journalConference articlepeer-review

    17 Citations (Scopus)

    Abstract

    Bottleneck features have been shown to be effective in improving the accuracy of speaker recognition, language identification and automatic speech recognition. However, few works have focused on bottleneck features for acoustic event recognition. This paper proposes a novel acoustic event recognition framework using bottleneck features derived from a Deep Neural Network (DNN). In addition to conventional features (MFCC, Mel-spectrum, etc.), this paper employs rhythm, timbre, and spectrum-statistics features for effectively extracting acoustic characteristics from audio signals. The effectiveness of the proposed method is demonstrated on a database of real life recordings via experiments, and its robust performance is verified by comparing to conventional methods.

    Original languageEnglish
    Pages (from-to)2954-2957
    Number of pages4
    JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
    Volume08-12-September-2016
    DOIs
    Publication statusPublished - 2016
    Event17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
    Duration: 2016 Sept 82016 Sept 16

    Bibliographical note

    Publisher Copyright:
    Copyright © 2016 ISCA.

    Keywords

    • Acoustic event recognition
    • Bottleneck feature
    • Deep belief network
    • Deep neural network
    • Feature extraction

    ASJC Scopus subject areas

    • Language and Linguistics
    • Human-Computer Interaction
    • Signal Processing
    • Software
    • Modelling and Simulation

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