Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    14 Citations (Scopus)

    Abstract

    In this paper, we propose a stacked convolutional and recurrent neural network (CRNN) with a 3D convolutional neural network (CNN) in the first layer for the multichannel sound event detection (SED) task. The 3D CNN enables the network to simultaneously learn the inter-and intra-channel features from the input multichannel audio. In order to evaluate the proposed method, multichannel audio datasets with different number of overlapping sound sources are synthesized. Each of this dataset has a four-channel first-order Ambisonic, binaural, and single-channel versions, on which the performance of SED using the proposed method are compared to study the potential of SED using multichannel audio. A similar study is also done with the binaural and single-channel versions of the real-life recording TUT-SED 2017 development dataset. The proposed method learns to recognize overlapping sound events from multichannel features faster and performs better SED with a fewer number of training epochs. The results show that on using multichannel Ambisonic audio in place of single-channel audio we improve the overall F-score by 7.5%, overall error rate by 10% and recognize 15.6% more sound events in time frames with four overlapping sound sources.

    Original languageEnglish
    Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
    PublisherIEEE
    ISBN (Electronic)9781509060146
    DOIs
    Publication statusPublished - 10 Oct 2018
    Publication typeA4 Article in a conference publication
    EventInternational Joint Conference on Neural Networks - Rio de Janeiro, Brazil
    Duration: 8 Jul 201813 Jul 2018

    Publication series

    Name
    ISSN (Electronic)2161-4407

    Conference

    ConferenceInternational Joint Conference on Neural Networks
    CountryBrazil
    CityRio de Janeiro
    Period8/07/1813/07/18

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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