Ulukaya, SezerSen, IpekKahya, Yasemin P.2024-06-122024-06-122015978-1-4673-7386-92165-0608https://hdl.handle.net/20.500.14551/2259323nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYAmong respiratory disorders, obstructive diseases such as asthma and chronic obstructive pulmonary disease (COPD) constitute an important group. To our knowledge, there does not exist a study in the literature that quantifies the relationship between the type of wheeze and the type or severity of the disease. This study, aims at classifying wheeze type rather than classical normal-wheeze sound classification studies in the literature. In this study, we propose a method based on Multiple Signal Classification (MUSIC) algorithm to differentiate between monophonic and polyphonic wheezes, without a need for pre-training the algorithm. The algorithm determines the true labels of monophonic and polyphonic wheezes with 100% and 78% accuracy, respectively. Since there does not exist a method in the literature that has been proposed specifically for this problem, only the results of the most relevant few studies have been presented. Since the proposed system can directly estimate the frequency, we consider the method proposed here would be a useful quantification method for further studies in medical literature, on finding correlations between wheezes and disorders.trinfo:eu-repo/semantics/closedAccessMUSICMonophonicPolyphonicFrequency EstimationSubspace MethodsLung SoundRespiratory SoundsConference Object20012004N/AWOS:0003805009004802-s2.0-84939151315N/A