Voice Detection Using Convolutional Neural Network

The article presents an approach, methodology, the software system based on a machine learning technologies for convolutional neural network and its use for voice (cough) recognition. Tasks of article are receiving evaluating a voice detection system with deep learning, the use of a convolutional neural network and Python language for patients with cough. The convolutional neural network has been developed, trained and tested using various datasets and Python libraries. Unlike the existing modern works related to this area, proposed system was evaluated using a real set of environmental sound data, and not only on filtered or separated voice audio tracks. The final compiled model showed a relatively high average accuracy of 85.37 %. Thus, the system is able to detect the sound of a voice in a crowded public place, and there is no need for a sound separation phase for pre-processing, as other modern systems require. Several volunteers recorded their voice sounds using microphones of their smartphones, and it was guaranteed that they would test their voices in public places to make noise, in addition to some audio files that were uploaded online. The results showed an average recognition accuracy – of 85.37 %, a minimum accuracy – of 78.8 % and a record – of 91.9 %.