Acoustic scene classification using projection Kervolutional neural network

Manjunath Mulimani, Ritika Nandi, Shashidhar G. Koolagudi

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalMultimedia Tools and Applications
DOIs
Publication statusAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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