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.
|Título de la publicación alojada||Smart Innovation, Systems and Technologies|
|Editorial||Springer Science and Business Media Deutschland GmbH|
|Número de páginas||10|
|Estado||Publicada - 2022|
Serie de la publicación
|Nombre||Smart Innovation, Systems and Technologies|
|ISSN (versión impresa)||2190-3018|
|ISSN (versión digital)||2190-3026|
Nota bibliográficaPublisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.