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Model-based approaches to achieve Single Channel Source Separation (SCSS) have been reasonably successful at separating two sources. However, most of the currently used model-based approaches require pre-trained speaker specific models in order to perform the separation. Often, insufficient or no prior training data may be available to develop such speaker specific models, necessitating the use of a speaker independent approach to SCSS. This paper proposes a speaker independent approach to SCSS using sinusoidal features. The algorithm develops speaker models for novel speakers from the speech mixtures under test, using prior training data available from other speakers. An iterative scheme improves the models with respect to the novel speakers present in the test mixtures. Experimental results indicate improved separation performance as measured by the Perceptual Evaluation of Speech Quality (PESQ) scores of the separated sources.