Publication detail

Neural Networks with Dilated Convolutions for Sound Event Recognition

MIKLÁNEK, Š.

Original Title

Neural Networks with Dilated Convolutions for Sound Event Recognition

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

Convolutional neural networks, most commonly deployed in image classification tasks, typically use square-shaped convolutional kernels, which are well suited for feature extraction from two-dimensional data. This study explores the effect of utilizing spectrally aware dilated convolutions specialized for sound event recognition. By extending the base kernels in the time or the frequency dimension, the features extracted from the spectral audio representations should, in theory, better capture the temporal and timbral information of different sound events. The baseline neural network model with squared kernels was compared against three models, which used an increasing dilation factor in the subsequent convolutional layers. The three models were purposefully tuned to focus towards the frequency and time feature extraction. The results have shown that the models with dilated convolutions performed noticeably better in comparison with the baseline model.

Keywords

sound event recognition, convolutional neural networks, dilated convolution

Authors

MIKLÁNEK, Š.

Released

13. 7. 2021

Location

Brno

ISBN

978-80-214-5942-7

Book

Proceedings I of the 27th Conference STUDENT EEICT 2021

Edition

1.

Pages count

5

BibTex

@inproceedings{BUT171286,
  author="Štěpán {Miklánek}",
  title="Neural Networks with Dilated Convolutions for Sound Event Recognition",
  booktitle="Proceedings I of the 27th Conference STUDENT EEICT 2021",
  year="2021",
  series="1.",
  pages="5",
  address="Brno",
  isbn="978-80-214-5942-7"
}