We present an online method to learn recurring time-frequency patterns from spectrograms. Our method relies on a convolutive decomposition that estimates sequences of spectra into time-frequency patterns and their corresponding activation signals. This method processes one spectrogram at a time such that in comparison with a batch method, the computational cost is reduced proportionally to the number of considered spectrograms. We use a first-order stochastic gradient descent and show that a monotonically decreasing learning-rate works appropriately. Furthermore, we suggest a framework to classify spectrograms based on the estimated set of time-frequency patterns. Results, on a set of synthetically generated spectrograms and a real-world dataset, show that our method finds meaningful time-frequency patterns and that it is suitable to handle a large amount of data.