This paper describes the frequency-domain approach to the blind source separation of speech/audio signals that are convolutively mixed in a real room environment. With the application of shorttime Fourier transforms, convolutive mixtures in the time domain can be approximated as multiple instantaneous mixtures in the frequency domain. We employ complex-valued independent component analysis (ICA) to separate the mixtures in each frequency bin. Then, the permutation ambiguity of the ICA solutions should be aligned so that the separated signals are constructed properly in the time domain. We propose a permutation alignment method based on clustering the activity sequences of the frequency bin-wise separated signals. We achieved the overall winner status of MLSP 2007 Data Analysis Competition based on the presented method.