_applied sciences_  
_Article_  
**Auditory Property-Based Features and Artificial Neural Network Classifiers for the Automatic Detection of Low-Intensity Snoring/Breathing Episodes**  
**[PERSON]**1\({}^{\copyright}\)**, **[PERSON]**2\({}^{\copyright}\)**, **[PERSON]**3\({}^{\copyright}\)**, **[PERSON]**4\({}^{\copyright}\)** and **[PERSON]**5  
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To resolve these problems, research is being conducted with the aim to establish an OSAS screening method that is based on noncontact microphones. These studies include (i) technological development to detect snoring/breathing episodes (SBEs) [8; 9; 10; 11; 12; 13], (ii) studies of snoring that characterize OSAS [14; 15; 16; 17; 18], and (iii) snoring-based OSAS screening methods and evaluations [19; 20; 21]. In [8; 9; 10; 11; 12; 13], snoring characteristics were extracted using ZCR, MFCC, and other statistical processing from the respiratory sounds during sleep which were obtained from patients; it was then shown that SBE sections could be classified using deep learning with accuracies in the range of 75.1-96.8% in various environments, including noise. In [14; 15; 16; 17; 18], the effectiveness of various characteristics in OSAS and non-OSAS patients in terms of temporal, frequency, intensity, and clinical features was evaluated to characterize OSAS-related upper airway obstruction. In [19; 20; 21], snoring sounds obtained from noncontact microphones were segmented, features were extracted by statistical processing and formant acoustic analysis, and machine learning tools, such as logistic regression and AdaBoost were used to classify OSAS/non-OSAS and sleep/waking states at sensitivities in the range of 80-90%, while doing so at a low cost. As shown in [22; 23; 24; 25], it has recently been suggested that sleep-awake activity and sleep quality could be estimated based on the analysis of respiratory sounds obtained during sleep. These results emphasized the importance of detecting SBEs during sleep. The automatic detection of SBEs from sleep sounds is the first step for automatic OSAS screening based on snoring. However, SBEs have a high dynamic range and are barely audible at intensities >90 dB. Specifically, there is a need for a method to automatically detect low-intensity SBEs without any contact, even in a low-SNR environment. Therefore, our research group has been developing a system that automatically detects low-intensity SBEs from sleep sounds obtained by noncontact recording [10; 12]. It has been suggested that the automatic detection of low-intensity SBEs has a high performance compared with other methods proposed in recent studies. However, the calculation speed and performance must be improved further for practical use. The purpose of our study was to develop a more efficient method to detect low-intensity SBEs in sleep sound recordings. Even if low-intensity SBEs are present in sleep sounds, human hearing can distinguish them from sleep sounds by careful listening. This is because the human auditory pathway has an innate function which is used to analyze the fine temporal characteristics of sound. The auditory image model (AIM) [26; 27; 28], which simulates a human auditory mechanism from an engineering perspective, was developed by [PERSON] in 1995 [26]. To generate a stabilized auditory image (SAI), this AIM describes a process of strobed temporal integration which transforms the signal flow from the cochlea up the auditory nerve to the brain. For sound event classification [13; 29; 1], front-end, ear-like audio analysis has been conducted by generating features extracted from an SAI. However, the calculation of SAI requires large computational and memory costs. Conversely, sound event detection performed based on the peaks corresponding to glottal pulses was apparent in the neural activity pattern (NAP) which was converted into an SAI [30; 31; 32].