## 4 Discussion and Conclusions
[51] proposed an automatic snore detection method using sound maps and a series of neural networks. The results demonstrated that the method is appropriate for identifying snores with an accuracy in the range of 91.8-95.1%. However, in this study, potential snoring episodes were segmented using the improved sub-band spectral entropy method which is based on sub-band energy calculation. [PERSON] et al. [8] proposed the use of the MFCC feature extraction method and the LSTM model for the binary classification of snoring data. The experimental results showed that the developed method yielded the highest accuracy rate of 87%. However, for the analysis, very weak snoring sounds were not labeled in the data presented on PSG used in this study. Furthermore, a sleep sound classification method based on AIM has been proposed for sleep sounds extracted by an energy-based approach, and it has been confirmed that sleep sounds can be classified with high accuracy [13]. However, there is a need to use multiple acoustic features obtained from SAI which is converted from NAP using strobed temporal integration (STI). This study has the following advantages. The proposed method can be conducted with low-computational costs because it eliminates the computationally expensive STI processing used in AIM and can be built using stages up to NAP. Additionally, it has been confirmed that the use of the proposed method allows for the detection of low-intensity SBEs with higher performance compared with our previous method [12], and the computational speed was also significantly improved. Given that the performance of the proposed method was superior to that of our previous method even in the case of Exp-2, it is suggested that the new feature (NAPCC) proposed in this study is an acoustic feature that is robust against noise. However, our study has some limitations: (i) a relatively small size of the dataset, which cannot satisfy sound variety, was used; (ii) for SBEs of short duration, the performance of the proposed method was degraded because the output of the NAPCC corresponding to the SBEs became small in the NAPCC spectrogram, which is the ordered series of the NAPCC of each frame for the recorded data; (iii) the proposed method uses the DCGA filter bank approach which has the highest calculation cost in the NAPCC calculation procedure. The proposed method is expected to contribute as a pretreatment step to OSAS screening based on snoring and respiratory sounds. It is thought to be useful for the effective and automatic identification of respiratory sound information, particularly apneic sections and silence, from sleep sounds acquired without contact. Conceptualization, T.E., O.J., N.T., and I.K. ; methodology, T.E. and K.H. ; software, K.H. ; validation, T.E. and K.H. ; formal analysis, K.H; investigation, T.E., K.H., and N.T. ; data curation, K.H., I.K., N.T., and O.J. ; writing--original draft preparation, K.H. ; writing--review and editing, T.E. ; project administration, T.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** This study was conducted after obtaining approval from the ethics review boards of the Division of Science and Technology, Graduate School of Technology, Industrial and Social Sciences (No. 20012), Tokushima University, and Anan Kyoei Hospital (No. 39). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors thank [PERSON] for his help with the data analysis of this study. **Conflicts of Interest:** The authors declare no conflict of interest.