The thesis project is aimed at designing an unobtrusive method to find the obstruction location and severity for patients who are not diagnosed with Obstructive sleep apnea, during their non-sedated sleep using simple recording devices within uncontrolled environment. Similar to speech generation, which is enabled by opening and closing of the vocal cords, the sound of snoring consists of a series of impulses caused by the rapid obstruction and reopening of the upper airway. By exploiting this similarity, we try to explain snoring sound production using synthesis techniques that has been applied to speech. A trial has been conducted to gain information on efficacy of different commercially available devices that are used to alleviate snoring problem. Sleeping sounds from this trial has been analyzed to find a method to find the effective device for each participant. Similar to speech analysis, Iterative Adaptive Inverse Filtering(IAIF) method has been used to find excitation flow and airway tract transfer functions of snoring sounds during the inhalation period. A model has been built using the Acoustic Tube Theory with the purpose of reflecting the physical realities of snoring sound production. Resulting transfer function spectra from acoustic tube modeling and IAIF has been compared using a gain-independent Itakura Spectral Distance Measure. It has been found that Linear Prediction is not suitable to directly determine the cross-sectional area of the upper airway from snoring sounds. An alternative method, using transmission line model with acoustic tube modeling has been found to produce transfer functions that are spectrally similar to transfer functions obtained from source filter models.