Effects of Data Augmentation on the Identification of Cough Sound Using Convolutional Neural Networks

David Naranjo, Juan Chica, Christian Salamea Palacios

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

Abstract

Cough identification employing deep learning models has generated significant interest in fields such as telemedicine. However, it is challenging to have a considerable number of cough sounds to train such models, and consequently, it is necessary to apply techniques to generate more data. In this work, the effect of data augmentation on the identification of cough was studied using convolutional neural networks (CNN). For this, a system composed of Front-End and Back-End was implemented. In the Front-End, the original recordings and those generated by data augmentation by frequency shift and noise injection were processed and transformed into Mel spectrograms. On the Back-End, the spectrograms were analyzed and classified using a CNN. The results show that the transformations proposed to increase the amount of data help reducing the overfitting by 42.5% while bettering accuracy by a relative improvement of 1%. Therefore, applying data augmentation techniques to train CNNs for differentiating coughs from other sound events results in a simple but effective strategy.

Original languageEnglish
Title of host publicationCommunication, Smart Technologies and Innovation for Society - Proceedings of CITIS 2021
EditorsÁlvaro Rocha, Paulo Carlos López-López, Juan Pablo Salgado-Guerrero
PublisherSpringer Science and Business Media Deutschland GmbH
Pages285-295
Number of pages11
ISBN (Print)9789811641251
DOIs
StatePublished - 2022
Event7th International Conference on Science, Technology and Innovation for Society, CITIS 2021 - Virtual, Online
Duration: 26 May 202128 May 2021

Publication series

NameSmart Innovation, Systems and Technologies
Volume252
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference7th International Conference on Science, Technology and Innovation for Society, CITIS 2021
CityVirtual, Online
Period26/05/2128/05/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

  • Convolutional neural networks
  • Cough identification
  • Data augmentation

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