We propose a novel approach to performing change-detection based on sparse representations and dictionary learning. We operate on observations that are finite support signals, which in stationary conditions lie within a union of low dimensional subspaces. We model changes as perturbations of these subspaces and provide an online and sequential monitoring solution to detect them. This approach allows extension of the change-detection framework to operate on streams of observations that are signals, rather than scalar or multivariate measurements, and is shown to be effective for both synthetic data and on bursts acquired by rockfall monitoring systems.