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A multi-branch convolutional neural network for snoring detection based on audio.
- Source :
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Computer Methods in Biomechanics & Biomedical Engineering . Feb2024, p1-12. 12p. 8 Illustrations, 4 Charts. - Publication Year :
- 2024
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Abstract
- AbstractObstructive sleep apnea (OSA) is associated with various health complications, and snoring is a prominent characteristic of this disorder. Therefore, the exploration of a concise and effective method for detecting snoring has consistently been a crucial aspect of sleep medicine. As the easily accessible data, the identification of snoring through sound analysis offers a more convenient and straightforward method. The objective of this study was to develop a convolutional neural network (CNN) for classifying snoring and non-snoring events based on audio. This study utilized Mel-frequency cepstral coefficients (MFCCs) as a method for extracting features during the preprocessing of raw data. In order to extract multi-scale features from the frequency domain of sound sources, this study proposes the utilization of a multi-branch convolutional neural network (MBCNN) for the purpose of classification. The network utilized asymmetric convolutional kernels to acquire additional information, while the adoption of one-hot encoding labels aimed to mitigate the impact of labels. The experiment tested the network’s performance by utilizing a publicly available dataset consisting of 1,000 sound samples. The test results indicate that the MBCNN achieved a snoring detection accuracy of 99.5%. The integration of multi-scale features and the implementation of MBCNN, based on audio data, have demonstrated a substantial improvement in the performance of snoring classification. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10255842
- Database :
- Academic Search Index
- Journal :
- Computer Methods in Biomechanics & Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 175516513
- Full Text :
- https://doi.org/10.1080/10255842.2024.2317438