# A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices +
## 2 Related Work  
The study in [5] presents a novel snore detection algorithm which uses convolutional recurrent neural networks. The model is evaluated on audio data from 38 users while they slept. The system uses microphones installed in different places, and the algorithm achieved high accuracy (95.3%) in detecting snore events, with 92.2% sensitivity and 97.7% specificity. The performance of the algorithm remained robust across different microphone positions, which indicates the reliability of the system for snore detection in different sleep environments. Another study [6], focuses on the crucial need for a reliable snoring detection system for monitoring and diagnosing obstructive sleep apnea (OSA) to improve the quality of life for those with the disorder. This research proposes a hybrid convolutional neural network (CNN) model for detecting snores. In OSA monitoring in real-world situations, the model achieved an 89.3% average classification accuracy, 89.7% sensitivity, and 88.5% specificity. The research in [7] discusses the health hazards associated with obstructive sleep apnea hypopnea syndrome (OSAHS) and suggests a novel strategy for monitoring and identification to avoid treatment delays. The system classifies the snoring voices of normal individuals and those with OSAHS. Mel-frequency cepstral coefficients (MFCCs) are used in the study, along with CNN and LSTM models for feature extraction. The method has the greatest accuracy rate of 87% for binary categorization of snoring data. The suggested approach can also estimate the severity of OSAHS using the determined AHI value, providing useful information for clinical diagnosis and therapy. In [8], the authors discuss the challenges of detecting OSAHS by developing a snore detection system that enables at-home screening. The study suggests an approach for utilizing the hardware limitation of smartphones. The developed system detects snoring and environmental noises with using a real-time snore detector (RTSD) for sleep sound recordings. The RTSD serves as a valuable standalone tool for analyzing quality of sleep. The research in [9] explores snoring issues and highlight their impact on health. The research aimed to develop a deep learning model implemented as an Android smartphone app for snore detection. The app analyzes real-time audio captured by the on-device microphone and classifies snore and non-snore voice and noise with an accuracy of 98%. The authors in [10] discussed an efficient method for snoring detection to diagnose OSA and health complications related to it. The study employs a CNN to distinguish between snoring and non-snoring voices and noises based on audio inputs. The raw data is preprocessed using MFCC, and multi-scale features are extracted from the frequency domain using a multi-branch CNN (MBCNN). The developed model achieves a snoring detection accuracy of 99.5%. The research in [11] addresses the issue of poor sleep quality in modern society and proposed a method to detect snoring and coughing episodes during sleep. The study discusses a three-stage method, consisting of the segmentation of nightly sound data into individual events, extraction of features from snoring and coughing episodes usingFourier transforms, and recognition of these events using the Support Vector Machine (SVM) and the Hidden Markov Model (HMM). Experimental results demonstrate the effectiveness of the method in accurately detecting snoring and coughing events. The study presented in [12] proposes a low-cost alternative to polysomnography (PSG) for detecting OSA. A repository of snoring audio recordings is processed using multi-threshold endpoint detection and feature extraction to obtain distinctive information. Machine learning models are then trained to predict feature categories. A real-time system for an embedded device is developed to detect snoring and OSA. A multi-classification temporal convolutional network (TCN) is trained to distinguish between non-snoring, snoring noises, and OSA-related snoring.