Snoring Sound Recognition Using Multi-Channel Spectrograms
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common and high-risk sleep-related breathing disorder. Snoring detection is a simple and non-invasive method. In many studies, the feature maps are obtained by applying a short-time Fourier transform (STFT) and feeding the model with single-chan...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2024-01-01
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| Series: | Archives of Acoustics |
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| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/3807 |
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| author | Ziqiang YE Jianxin PENG Xiaowen ZHANG Lijuan SONG |
| author_facet | Ziqiang YE Jianxin PENG Xiaowen ZHANG Lijuan SONG |
| author_sort | Ziqiang YE |
| collection | DOAJ |
| description | Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common and high-risk sleep-related breathing disorder. Snoring detection is a simple and non-invasive method. In many studies, the feature maps are obtained by applying a short-time Fourier transform (STFT) and feeding the model with single-channel input tensors. However, this approach may limit the potential of convolutional networks to learn diverse representations of snore signals. This paper proposes a snoring sound detection algorithm using a multi-channel spectrogram and convolutional neural network (CNN). The sleep recordings from 30 subjects at the hospital were collected, and four different feature maps were extracted from them as model input, including spectrogram, Mel-spectrogram, continuous wavelet transform (CWT), and multi-channel spectrogram composed of the three single-channel maps. Three methods of data set partitioning are used to evaluate the performance of feature maps. The proposed feature maps were compared through the training set and test set of independent subjects by using a CNN model. The results show that the accuracy of the multi-channel spectrogram reaches 94.18%, surpassing that of the Mel-spectrogram that exhibits the best performance among the single-channel spectrograms. This study optimizes the system in the feature extraction stage to adapt to the superior feature learning capability of the deep learning model, providing a more effective feature map for snoring detection. |
| format | Article |
| id | doaj-art-8b30ab83b09949daa55108fc33e73471 |
| institution | Kabale University |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-8b30ab83b09949daa55108fc33e734712025-08-20T03:34:00ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2024-01-0149210.24425/aoa.2024.148775Snoring Sound Recognition Using Multi-Channel SpectrogramsZiqiang YE0Jianxin PENG1Xiaowen ZHANG2Lijuan SONG3South China University of TechnologySouth China University of TechnologyGuangzhou Medical UniversityGuangzhou Medical UniversityObstructive sleep apnea-hypopnea syndrome (OSAHS) is a common and high-risk sleep-related breathing disorder. Snoring detection is a simple and non-invasive method. In many studies, the feature maps are obtained by applying a short-time Fourier transform (STFT) and feeding the model with single-channel input tensors. However, this approach may limit the potential of convolutional networks to learn diverse representations of snore signals. This paper proposes a snoring sound detection algorithm using a multi-channel spectrogram and convolutional neural network (CNN). The sleep recordings from 30 subjects at the hospital were collected, and four different feature maps were extracted from them as model input, including spectrogram, Mel-spectrogram, continuous wavelet transform (CWT), and multi-channel spectrogram composed of the three single-channel maps. Three methods of data set partitioning are used to evaluate the performance of feature maps. The proposed feature maps were compared through the training set and test set of independent subjects by using a CNN model. The results show that the accuracy of the multi-channel spectrogram reaches 94.18%, surpassing that of the Mel-spectrogram that exhibits the best performance among the single-channel spectrograms. This study optimizes the system in the feature extraction stage to adapt to the superior feature learning capability of the deep learning model, providing a more effective feature map for snoring detection.https://acoustics.ippt.pan.pl/index.php/aa/article/view/3807obstructive sleep apnea-hypopnea syndromesnoringconvolutional neural networkmulti-channel spectrogram |
| spellingShingle | Ziqiang YE Jianxin PENG Xiaowen ZHANG Lijuan SONG Snoring Sound Recognition Using Multi-Channel Spectrograms Archives of Acoustics obstructive sleep apnea-hypopnea syndrome snoring convolutional neural network multi-channel spectrogram |
| title | Snoring Sound Recognition Using Multi-Channel Spectrograms |
| title_full | Snoring Sound Recognition Using Multi-Channel Spectrograms |
| title_fullStr | Snoring Sound Recognition Using Multi-Channel Spectrograms |
| title_full_unstemmed | Snoring Sound Recognition Using Multi-Channel Spectrograms |
| title_short | Snoring Sound Recognition Using Multi-Channel Spectrograms |
| title_sort | snoring sound recognition using multi channel spectrograms |
| topic | obstructive sleep apnea-hypopnea syndrome snoring convolutional neural network multi-channel spectrogram |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/3807 |
| work_keys_str_mv | AT ziqiangye snoringsoundrecognitionusingmultichannelspectrograms AT jianxinpeng snoringsoundrecognitionusingmultichannelspectrograms AT xiaowenzhang snoringsoundrecognitionusingmultichannelspectrograms AT lijuansong snoringsoundrecognitionusingmultichannelspectrograms |