1. A learning system for signal processing, comprising: N classification layers associated with MN probabilistic models per layer, N and M being integers, respectively; at least one parameter defined per a respective classification layer, the parameters trained independently at different classification layers; and at least one input stream that is analyzed by the N classification layers, the at least one input stream sampled according to varying levels of temporal granularity at the respective classification layers.