Cough Sound Identification: An Approach Based on Ensemble Learning

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

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Resumen

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.

Idioma originalInglés
Título de la publicación alojadaSmart Innovation, Systems and Technologies
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas269-278
Número de páginas10
DOI
EstadoPublicada - 2022

Serie de la publicación

NombreSmart Innovation, Systems and Technologies
Volumen279
ISSN (versión impresa)2190-3018
ISSN (versión digital)2190-3026

Nota bibliográfica

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

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