REPET-SIM is a generalization of the REpeating Pattern Extraction Technique (REPET) that uses a similarity matrix to separate the repeating background from the non-repeating foreground in a mixture. The method assumes that the background (typically the music accompaniment) is dense and low-ranked, while the foreground (typically the singing voice) is sparse and varied. While this assumption is often true for background music and foreground voice in musical mixtures, it also often holds for background noise and foreground speech in noisy mixtures. We therefore propose here to extend REPET-SIM for noise/speech segregation. In particular, given the low computational complexity of the algorithm, we show that the method can be easily implemented online for real-time processing. Evaluation on a data set of 10 stereo two-channel mixtures of speech and real-world background noise showed that this online REPET-SIM can be successfully applied for real-time speech enhancement, performing as well as different competitive methods.