In this paper a modular approach to single-microphone source separation is proposed. A probabilistic model for mixtures of observations is constructed, where the independent underlying source signals are described by non-linear autoregressive models. Source separation in this model is achieved by performing online probabilistic inference through an efficient message passing procedure. For retaining tractability with the non-linear autoregressive models, three different approximation methods are described. A set of experiments shows the effectiveness of the proposed source separation approach. The source separation performance of the different approximation methods is quantified through a set of verification experiments. Our approach is validated in a speech denoising task.