Sound-Based Cough Detection System using Convolutional Neural Network

Sound recording and processing techniques can be used in designing diagnostic solutions for a variety of medical conditions related to the respiratory system. In this spectrum, cough monitoring for chronic or seasonal conditions is a significant medical practice. In this paper, a precise cough identification and monitoring system is presented. The system is utilising a convolutional neural network as a feature extraction algorithm and classification system. Including several functions of loading the audio data into the system and converting it into a set of spectrograms, as well as the pre-segmentation stage function, the model retains its relatively low-complexity, which allows accelerating the learning process, also enhanced using dropout. Due to limited audio data available, the dataset dimension was established at 600 samples, split into two equal-numbered groups - 300 samples of “cough” samples, and 300 of “non-cough” samples. The validation accuracy (thus the percentage of samples labelled correctly by the system during the validation process) yielded over 84%, suggesting that this can be a successful cough detection method for future medical applications and devices, such as potential respiratory system condition diagnostic tool.