## 4 Discussion and Conclusions  
In this study, we proposed a new method that used the auditory property-based features of NAPCC and ANN discriminators to automatically detect low-intensity SBEs from recorded data that were acquired using a noncontact microphone. The effectiveness of the proposed method was investigated by detecting SBEs from recorded data of 25 individuals which comprised silence and SBEs (Exp-1) and from recorded data of 15 individuals which included non-SBEs thought to be noise, generated in an actual environment (Exp-2). Leave-one-out cross-validation was used to evaluate the performance in each experiment. The results suggested that SBEs could be detected with an average accuracy of 85.83% in Exp-1 and of 85.99% in Exp-2. A comparison of performance with the MLP-ANN-based SBE detection method proposed in our previous work [12] showed that the proposed method was approximately 3% better in the case of Exp-1 and approximately 10% better in the case of Exp-2. In particular, the standard deviation in both Exp-1 and Exp-2 became smaller with the proposed method when compared with the conventional method. Hence, it is thought that the influence of each individual subject could be reduced. Furthermore, the large improvement in specificity and PPV suggests that the new method is useful for the effective and automatic detection of silent or apneic sections contained in sleep sounds. To date, gammatone frequency cepstral coefficients (GFCC) [48] and BMM-based cepstrum coefficients [49] have been developed, but both require gammatone filter bank outputs or mean normalization. To achieve a more accurate human auditory perception, a DCGA filter was developed to extend the domain of the gammatone auditory filter. This filter bank accommodates the nonlinear behavior observed in human psychophysics and can be useful for perceptual signal processing [50]. The DCGA filter bank, which is the front-end of NAPCC, may be more noise-robust than the gammatone filter bank. Furthermore, the results obtained in this work showed that the noise robustness of NAPCC was improved by using the sigmoid normalization instead of the mean normalization used in GFCC and BMM-based cepstrum coefficients. Therefore, NAPCC which uses DCGA \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Types of Classifiers Used** & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** & **PPV [\%]** & **NPV [\%]** & **F1 Score [\%]** \\ \hline MLP-ANN & 86.14 \(\pm\) 6.96 & 80.97 \(\pm\) 10.58 & 91.96 \(\pm\) 7.29 & 83.84 \(\pm\) 16.83 & 86.18 \(\pm\) 12.74 & 80.58 \(\pm\) 10.72 \\ RBF-ANN & 86.08 \(\pm\) 7.07 & 81.28 \(\pm\) 10.66 & 91.64 \(\pm\) 7.24 & 83.34 \(\pm\) 16.81 & 86.29 \(\pm\) 12.80 & 80.53 \(\pm\) 10.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance results of the proposed method based on the different classifiers applied on a dataset constructed by mixing data from Exp-1 and Exp-2. Figure 5: F1 score of the subjects obtained via the proposed or our previous method. (**a**) Exp-1 and (**b**) Exp-2 cases. and sigmoid normalization should improve the performance of GFCC and BMM-based cepstrum coefficients. In particular, to detect low-intensity SBEs in the sleep sound recordings, the method developed in our previous study in 2018 outperformed the recent techniques published up to 2018 [12]. Since 2018, more recent technologies have been developed to classify snoring episodes and non-snoring episodes from sleep sounds. [PERSON] et al. proposed a recurrent neural network (RNN)-based classification method that was capable of classifying snoring episodes and non-snoring episodes with the use of features obtained from the sleep data recorded with a smartphone. The RNN-based classifiers achieved an accuracy of 98.9% using relatively small datasets [9]. However, snoring segments used in this work were created based on the peak point of snoring signals obtained via the peak-detection algorithm. This means that relatively high-intensity snoring was selected for the analysis. [PERSON] et al.