This paper considers underdetermined blind source separation of super-Gaussian signals that are convolutively mixed. The separation is performed in three stages. In the first stage, the mixing matrix in each frequency bin is estimated by the proposed single source detection and clustering (SSDC) algorithm. In the second stage, by assuming complex-valued super-Gaussian distribution, the sources are estimated by minimizing a mean-square-error (MSE) criterion. Special consideration is given to reduce computational load without compromising accuracy. In the last stage, the estimated sources in each frequency bin are aligned for recovery. In our simulations, the proposed algorithm outperformed conventional algorithm in terms of the mixing-error-ratio and the signal-to-distortion ratio.