Proceedings Article10.21437/INTERSPEECH.2021-656
Acoustic Scene Classification Using Kervolution-Based SubSpectralNet
Ritika Nandi,Shashank Shekhar,Manjunath Mulimani +2 more
- 30 Aug 2021
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TL;DR: The linear convolutional operation of SubSpectralNet is replaced with a non-linear operation using the kernel trick and the proposed method achieves 73.52% and 75.76% accuracy with Polynomial and Gaussian Kernels respectively.
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Abstract: In this paper, a Kervolution-based SubSpectralNet model is proposed for Acoustic Scene Classification (ASC). SubSpectralNet is a competitive model which divides the mel spectrogram into horizontal slices termed as sub-spectrograms that are considered as input to the Convolutional Neural Network (CNN). In this work, the linear convolutional operation of SubSpectralNet is replaced with a non-linear operation using the kernel trick. This is also known as kervolution (kernel convolution)-based SubSpectralNet. The performance of the proposed methodology is evaluated on the DCASE (Detection and Classification of Acoustic Scenes and Events) 2018 development dataset. The proposed method achieves 73.52% and 75.76% accuracy with Polynomial and Gaussian Kernels respectively.
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Citations
Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices
Lam Pham,Khoa Dinh,Dat T. Ngo,Hieu Tang,Alexander Schindler +4 more
- 23 Mar 2022
TL;DR: A robust and low complexity system for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording, in which a novel inception-residual-based network architecture is proposed to deal with the mismatched recording device issue.
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AtResNet: Residual Atrous CNN with Multi-scale Feature Representation for Low Complexity Acoustic Scene Classification
Aswathy Madhu,K. Suresh +1 more
TL;DR: The AtResNet, a residual atrous CNN for low complexity acoustic scene classification is introduced and it is suggested that the method significantly outperformed the CNN-based techniques in addition to a baseline system based on log mel spectrum for signal representation.
1
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