Computer Science > Sound
[Submitted on 28 Jan 2023]
Title:who is snoring? snore based user recognition
View PDFAbstract:Snoring is one of the most prominent symptoms of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAH), a highly prevalent disease that causes repetitive collapse and cessation of the upper airway. Thus, accurate snore sound monitoring and analysis is crucial. However, the traditional monitoring method polysomnography (PSG) requires the patients to stay at a sleep clinic for the whole night and be connected to many pieces of equipment. An alternative and less invasive way is passive monitoring using a smartphone at home or in the clinical settings. But, there is a challenge: the environment may be shared by people such that the raw audio may contain the snore activities of the bed partner or other person. False capturing of the snoring activity could lead to critical false alarms and misdiagnosis of the patients. To address this limitation, we propose a hypothesis that snore sound contains unique identity information which can be used for user recognition. We analyzed various machine learning models: Gaussian Mixture Model (GMM), GMM-UBM (Universial Background Model), and a Deep Neural Network (DNN) on MPSSC - an open source snoring dataset to evaluate the validity of our hypothesis. Our results are promising as we achieved around 90% accuracy in identification and verification tasks. This work marks the first step towards understanding the practicality of snore based user monitoring to enable multiple healthcare applicaitons.
Current browse context:
cs.SD
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.