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Acoustic scene classification using projection Kervolutional neural network

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Abstract

In this paper, a novel Projection Kervolutional Neural Network (ProKNN) is proposed for Acoustic Scene Classification (ASC). ProKNN is a combination of two special filters known as the left and right projection layers and Kervolutional Neural Network (KNN). KNN replaces the linearity of the Convolutional Neural Network (CNN) with a non-linear polynomial kernel. We extend the ProKNN to learn from the features of two channels of audio recordings in the initial stage. The performance of the ProKNN is evaluated on the two publicly available datasets: TUT Urban Acoustic Scenes 2018 and TUT Urban Acoustic Scenes Mobile 2018 development datasets. Results show that the proposed ProKNN outperforms the existing systems with an absolute improvement of accuracy of 8% and 14% on TUT Urban Acoustic Scenes 2018 and TUT Urban Acoustic Scenes Mobile 2018 development datasets respectively, as compared to the baseline model of Detection and Classification of Acoustic Scene and Events (DCASE) - 2018 challenge.

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Data Availability

The datasets used in this study are publicly available at the Zenodo repository at the following addresses.

– TUT Urban Acoustic Scenes 2018 development dataset: https://doi.org/10.5281/zenodo.1228142

– TUT Urban Acoustic Scenes Mobile 2018 development dataset: https://doi.org/10.5281/zenodo.1228235

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576 104, India

    Manjunath Mulimani & Ritika Nandi

  2. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, 575 025, India

    Shashidhar G Koolagudi

Authors
  1. Manjunath Mulimani
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  2. Ritika Nandi
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  3. Shashidhar G Koolagudi
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Correspondence to Manjunath Mulimani.

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Mulimani, M., Nandi, R. & Koolagudi, S.G. Acoustic scene classification using projection Kervolutional neural network. Multimed Tools Appl 82, 9447–9457 (2023). https://doi.org/10.1007/s11042-022-13763-6

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  • DOI: https://doi.org/10.1007/s11042-022-13763-6

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