The characterization of acoustic sources is of great interest in many industrial applications, in particular for the aeronautic or automotive industry for the development of new products. While localization of sources using observations from a wind tunnel is a well-known subject, the characterization and separation of the sources still needs to be explored. We present here a Bayesian approach for sources separation. Two prior modeling of the sources are considered: a sparsity inducing prior in the frequency domain and an auto-regressive model in the time domain. The proposed methods are evaluated on synthetic data simulating noise sources emitting from an airfoil inside a wind tunnel.