Accelerometer-Based Respiratory Sound & Bioacoustic Dataset

CICERONE Project — Bioacoustic Signal Dataset (Respiratory Sound, Cough & Non-Cough Analysis) CICERONE Project: Accelerometer-Based Respiratory Sound & Bioacoustic Dataset Cough & Non-Cough Event Signal Analysis This repository contains the processed bioacoustic time-series dataset generated within the CICERONE Project and used in the publication: Sanchez-Morillo, D., Sales-Lerida, D., Priego-Torres, B., & León-Jiménez, A. (2024). Cough Detection Using Acceleration Signals and Deep Learning Techniques. Electronics, 13(12), 2410. https://doi.org/10.3390/electronics13122410 To train and validate artificial intelligence-based models, experimental data were collected from a group of 23 participants (14 men and 9 women). The main objective of the dataset is the automatic detection and classification of voluntary cough, differentiating cough events from other acoustic and dynamic non-cough events (such as speech, laughter, throat clearing, or background noise). Funding: Grant PID2021-126810OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.