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This paper proposes a general single channel source separation approach that exploits statistical characteristics of the source including sparseness. A new observation model for a mixture of sparse sources is introduced for this purpose. With this approach, source separation is achieved by iterating two simple sub-procedures, namely the clustering of the time-frequency (TF) bins into individual sources and the separate updating of the model parameters of each source. An advantage of this approach is that we can update the model parameters of each source assuming each cluster to contain a single source, and thus we can utilize the various model parameter estimation algorithms used for single source analysis, which can be simple and accurate, in an efficient and unified manner. We implement a harmonicity based source separation method with this approach using a robust fundamental frequency (F0) estimation algorithm. The experimental results confirm the effectiveness of the proposed method.