Snoring Sound Classification Using 1D-CNN Model Based on Multi-Feature Extraction

Abstract

Sound is an essential element of human relationships and communication. The sound recognition process involves three phases: signal preprocessing, feature extraction, and classification. This paper describes research on the classification of snoring data used to determine the importance of sleep health in humans. However, current sound classification methods using deep learning approaches do not yield desirable results for building good models. This is because some of the salient features required to sufficiently discriminate sounds and improve the accuracy of the classification are poorly captured during training. In this study, we propose a new convolutional neural network (CNN) model for sound classification using multi-feature extraction. The extracted features were used to form a new dataset that was used as the input to the CNN. Experiments were conducted on snoring and non-snoring datasets. The accuracy of the proposed model was 99.7% for snoring sounds, demonstrating an almost perfect classification and superior results compared to existing methods.