Relevant bibliographies by topics / Snoring Sound Analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Snoring Sound Analysis'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Snoring Sound Analysis"

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Hayashi, Shota, Meiyo Tamaoka, Tomoya Tateishi, Yuki Murota, Ibuki Handa, and Yasunari Miyazaki. "A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis." International Journal of Environmental Research and Public Health 17, no. 8 (2020): 2951. http://dx.doi.org/10.3390/ijerph17082951.

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The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients’ sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient’s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlati
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Yao, Yuhe, Jiecheng Zhu, Shaowei Guo, Wei Liu, Li Ding, and Jianxin Peng. "Acoustic analysis of snoring sound from different microphones." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 5 (2021): 1823–32. http://dx.doi.org/10.3397/in-2021-1962.

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Snoring is a common symptom of obstructive sleep apnea-hypopnea syndrome. The results show that there are obvious differences for most microphones in terms of the data distribution of features in the time and frequency domain. The results of snoring analysis from different recordings devices would be totally divergent. In view of this, when developing snoring analysis devices based user selected microphones (i.e. smartphone) recorded, we should take into account the discrepancy between different microphones.
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Wang, Lurui, and Zhongwei Jiang. "Tidal Volume Level Estimation Using Respiratory Sounds." Journal of Healthcare Engineering 2023 (February 16, 2023): 1–12. http://dx.doi.org/10.1155/2023/4994668.

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Respiratory sounds have been used as a noninvasive and convenient method to estimate respiratory flow and tidal volume. However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels during sleep qualitatively. Respiratory sounds are filtered and segmented into one-minute clips, all clips are clustered into three categories: normal breathing/snoring/uncertain with agglomerative hierarchical clustering (AHC). Formant parameters are extracted to classify snoring clips into simple snori
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Fang, Yu, Dongbo Liu, Sixian Zhao, and Daishen Deng. "Improving OSAHS Prevention Based on Multidimensional Feature Analysis of Snoring." Electronics 12, no. 19 (2023): 4148. http://dx.doi.org/10.3390/electronics12194148.

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Obstructive Sleep Apnea–Hypopnea Syndrome (OSAHS), a severe respiratory sleep disorder, presents a significant threat to human health and even endangers life. As snoring is the most noticeable symptom of OSAHS, identifying OSAHS via snoring sound analysis is vital. This study aims to analyze the time-domain and frequency-domain characteristics of snoring sounds to detect OSAHS and its severity. The snoring sounds are extracted and scrutinized from nighttime acoustic signals, with spectral energy ratio features being applied, calculated via the snore detection frequency division method. A varie
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Seren, Erdal, İlker İlhanlı, Nuray Bayar Muluk, Cemal Cingi, and Deniz Hanci. "Telephonic Analysis of the Snoring Sound Spectrum." Annals of Otology, Rhinology & Laryngology 123, no. 11 (2014): 758–64. http://dx.doi.org/10.1177/0003489414538401.

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Herzog, Michael, Thomas Bremert, Beatrice Herzog, Werner Hosemann, Holger Kaftan, and Alexander Müller. "Analysis of snoring sound by psychoacoustic parameters." European Archives of Oto-Rhino-Laryngology 268, no. 3 (2010): 463–70. http://dx.doi.org/10.1007/s00405-010-1386-9.

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Sadaoka, Tatsuya, Ryuichi Kanai, Noriya Kakitsuba, Yuki Fujiwara, and Hiroaki Takahashi. "Peculiar Snoring in Patients with Multiple System Atrophy: Its Sound Source, Acoustic Characteristics, and Diagnostic Significance." Annals of Otology, Rhinology & Laryngology 106, no. 5 (1997): 380–84. http://dx.doi.org/10.1177/000348949710600504.

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It is known that abductor paralysis (AP) of the vocal folds sometimes occurs in patients with multiple system atrophy (MSA), and some of them have sleep apnea and loud snoring during sleep. However, the site of obstruction and the sound source of the snoring are still unknown. We performed fiberscopic examinations under diazepam sedation in 8 MSA patients with AP and analyzed the snoring sound. We found that the peculiar snoring occurred with inspiratory vibration of the vocal folds, and there was no obstruction in this portion. Acoustic analysis showed that the fundamental frequency of vocal
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Peng, Hao, Huijie Xu, Zhan Gao, Weining Huang, and Yuxia He. "Acoustic analysis of overnight consecutive snoring sounds by sound pressure levels." Acta Oto-Laryngologica 135, no. 8 (2015): 747–53. http://dx.doi.org/10.3109/00016489.2015.1027414.

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QUINN, S. J., L. HUANG, P. D. M. ELLIS, and J. E. FFOWCS WILLIAMS. "The differentiation of snoring mechanisms using sound analysis." Clinical Otolaryngology 21, no. 2 (1996): 119–23. http://dx.doi.org/10.1111/j.1365-2273.1996.tb01313.x.

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Fiz, J. A., J. Abad, R. Jané, et al. "Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnoea." European Respiratory Journal 9, no. 11 (1996): 2365–70. http://dx.doi.org/10.1183/09031936.96.09112365.

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Dissertations / Theses on the topic "Snoring Sound Analysis"

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Hunt, Stephanie L. "Collagen Crosslinking Reagent Utilized to Modify the Mechanical Properties of the Soft Palate in Equine Snoring and Apnea Applications." UKnowledge, 2015. http://uknowledge.uky.edu/cbme_etds/36.

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Snoring is a sleep disruption that can lead to obstructive sleep apnea (OSA), which interrupts breathing by obstructing the airway. Injecting a protein crosslinker, such as genipin, into the soft palate could decrease the severity of snoring and OSA by stiffening the soft palate. Equine soft palates modeled human palates due to a high incidence of awake snoring and apnea. The pilot in vivo study treated six horses with two 100 mM injections of the buffered genipin reagent. The efficacy phase horses underwent respiratory audio recordings to document snoring changes using Matlab and ImageJ in th
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Azarbarzin, Ali. "Snoring sounds analysis: automatic detection, higher order statistics, and its application for sleep apnea diagnosis." IEEE, 2011. http://hdl.handle.net/1993/9593.

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Snoring is a highly prevalent disorder affecting 20-40% of adult population. Snoring is also a major indicative of obstructive sleep apnea (OSA). Despite the magnitude of effort, the acoustical properties of snoring in relation to physiological states are not yet known. This thesis explores statistical properties of snoring sounds and their association with OSA. First, an unsupervised technique was developed to automatically extract the snoring sound segments from the lengthy recordings of respiratory sounds. This technique was tested over 5665 snoring sound segments of 30 participants and
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林裕凱. "Feature Selection in Hierarchical Classification of Human Sounds and Acoustic Analysis of Snoring Signals." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/65730293916126523265.

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碩士<br>國立政治大學<br>資訊科學學系<br>96<br>Human sounds can be roughly divided into two categories: speech and non-speech. Traditional audio scene analysis research puts more emphasis on the classification of audio signals into human speech, music, and environmental sounds. We take a different perspective in this thesis. We are mainly interested in the analysis of non-speech human sounds, including laugh, scream, sneeze, and snore. Toward this goal, we investigate many commonly used acoustic features and select useful ones for classification using multivariate adaptive regression splines (MARS) and suppo
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Book chapters on the topic "Snoring Sound Analysis"

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Pramkeaw, Patiyuth, Penpichaya Lertritchai, and Nipaporn Klangsakulpoontawee. "Real-Time Snoring Sound Detecting U Shape Pillow System Using Data Analysis Algorithm." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60675-0_13.

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Wongsirichot, Thakerng, Nantanat Iad-ua, and Jutatip Wibulkit. "A Snoring Sound Analysis Application Using K-Mean Clustering Method on Mobile Devices." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26227-7_74.

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Conference papers on the topic "Snoring Sound Analysis"

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Liao, Wen-Hung, and Yu-Kai Lin. "Classification of non-speech human sounds: Feature selection and snoring sound analysis." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346556.

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Markandeya, Mrunal N., and Udantha R. Abeyratne. "Smart Phone based Snoring Sound analysis to Identify Upper Airway Obstructions." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857016.

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Eder, Guilleminault, and Penzel. "Detection And Analysis Of Respiratory Airflow And Snoring Sounds During Sleep Using Laryngeal Sound Discrimination." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.592926.

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Xiao, Li, Xiuping Yang, Xinhong Li, et al. "A Snoring Sound Dataset for Body Position Recognition: Collection, Annotation, and Analysis." In INTERSPEECH 2023. ISCA, 2023. http://dx.doi.org/10.21437/interspeech.2023-1430.

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Eder, Derek N., Christian Guilleminault, and Thomas Penzel. "Detection and analysis of respiratory airflow and snoring sounds during sleep using laryngeal sound discrimination (LSD)." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5761637.

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Wang, Junshi, Pan Han, Yaselly Sanchez, Jinxiang Xi, and Haibo Dong. "Computational Analysis on Aerodynamics and Vortex Formation of Sleep Apnea." In ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fedsm2018-83257.

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The fluid dynamics of air flow in the pharynx is critical to the vibration of the uvula and to the generation of the snoring sound. In this work, a combined experimental and computational approach was conducted to study the aerodynamics of the flow field in the human airway. An anatomically accurate pharynx model associated with different uvula kinematics was reconstructed from human magnetic resonance images (MRI) and high-speed photography. An immersed-boundary-method (IBM)-based direct numerical simulation (DNS) flow solver was used to simulate the corresponding unsteady flows in all their
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Tagluk, M. Emin, Mehmet Akin, and Necmettin Sezgin. "Time-frequency analysis of snoring sounds in patients with simple snoring and OSAS." In 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU). IEEE, 2009. http://dx.doi.org/10.1109/siu.2009.5136390.

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Markandeya, Mrunal, Udantha R. Abeyratne, Roneel V. Sharan, Craig Hukins, Brett Duce, and Karen McCloy. "Severity Analysis of Upper Airway Obstructions: Oesophageal Pressure Versus Snoring Sounds." In 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2019. http://dx.doi.org/10.1109/biocas.2019.8919149.

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Praydas, Thanawan, Booncharoen Wongkittisuksa, and Sawit Tanthanuch. "Obstructive Sleep Apnea Severity Multiclass Classification Using Analysis of Snoring Sounds." In The 2nd World Congress on Electrical Engineering and Computer Systems and Science. Avestia Publishing, 2016. http://dx.doi.org/10.11159/icbes16.142.

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Oliveira de Mattos Leon, Giuliana, Érico Marcelo Hoff do Amaral, and Julio Saraçol Domingues Júnior. "Ausculsensor: Uma exploração no Espaço de Projeto para Ausculta Pulmonar Automática na Fisioterapia." In Computer on the Beach. Universidade do Vale do Itajaí, 2022. http://dx.doi.org/10.14210/cotb.v13.p172-179.

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Respiratory diseases are one of the main causes of death in the world, being among the global priorities proposed by the World Health Organization (WHO). Pulmonary auscultation is the main method of physical examination of the thorax that leads to a more accurate analysis of pulmonary function to detect respiratory diseases. This procedure is used by its functionality, as it is a non-invasive procedure and also because of the fast detection of abnormalities. Due to Coronavirus Disease (COVID-19), this technique has become essential to determine the severity of the disease, as it makes it possi
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