TIFCORR is a Blind Signal Separation technique that is well suited to separating audio signals, requiring each signal to be sparse in only a local time-frequency region of their representation [1]. TIFCORR can suffer from inconsistencies in mixing system estimation, thus we present a modified algorithm incorporating k-means clustering [2] to improve estimation robustness. To improve the data efficiency ofTIFCORR, we also include an adaptive weighting function for mixing column estimates. These modifications transform our algorithm into a block adaptive algorithm with the ability to track time-varying mixtures.