Cough Sound Identification: An Approach Based on Ensemble Learning

Christian Salamea-Palacios, Javier Guaña-Moya, Tarquino Sanchez, Xavier Calderón, David Naranjo

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Cough identification using DSP techniques in an audio signal is a complex task; thus, an artificial intelligence approach is proposed by applying machine learning, deep learning, and HMMs algorithms. Later, an ensemble learning model has been used to differentiate cough from other environmental sounds, putting those algorithms together and choosing the best result as the performance of the system. The final system consists of a preprocessing stage where the audio signals are adjusted through data augmentation, normalization, removal of silent fragments, and the transformation to Mel spectrograms, while, on back-end stage, three models have been evaluated: a convolutional neural network, a random forest, and a classifier based on hidden Markov models. We assembled a hard voting classifier (VC) model from the three models to obtain a more robust and reliable model. The VC model reached the highest precision and F1-score values without false-negative and up to 75% of true-positive values.

Original languageEnglish
Title of host publicationSmart Innovation, Systems and Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
StatePublished - 2022

Publication series

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

Bibliographical note

Funding Information:
To the Corporaci?n Ecuatoriana para el Desarrollo de la Investigaci?n y Academia, CEDIA, for the financing provided to research, development, and innovation through the CEPRA projects, especially the project CEPRA-XV-2021-011: Caracterizaci?n de la tos provocada por el COVID-19 en pacientes con diagn?stico positivo. The authors thank Escuela Polit?cnica Nacional, Universidad Polit?cnica Salesiana, and Pontificia Universidad Cat?lica del Ecuador.

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


  • Convolutional neural network
  • Cough identification
  • Ensemble learning
  • Hidden Markov model
  • Random forest
  • Voting classifier


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