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With the emerging use of snore properties for clinical purposes, there is a need to understand the characteristics of snore source flow (SF)-the acoustic source in snore production. This paper attempts to analyze and model both SF and its derivative (SFD), along with its preliminary application to the generation of synthetic snores. SFs and SFDs were extracted from natural snores via an iterative adaptive inverse filtering approach, and subsequently parameterized into various time- and amplitude-based parameters to quantify the oscillatory maneuvers of snore excitation source (ES). The SF and SFD waveforms were also, respectively, modeled using the first and second derivatives of the Gaussian probability density function. Subjective and objective measures, including paired comparison score and sum-of-squared error, were assessed to appraise the performance of SFD model in producing natural-sounding snores. Results consistently show that: 1) the shapes of SF pulse are different among snores and can be associated with the dynamic biomechanical properties (e.g., compliance and elasticity) of ES; 2) changes to the SF or SFD pulse shape can affect the snore properties, both acoustically and perceptually; and 3) the proposed SFD model can generate close-to-natural sounding snores. Further research in this area can potentially yield valuable benefits to snore-oriented applications.