ABSTRACT
The processing of continuous acoustic signals is a challenging problem for perceptual and cognitive models. Sound patterns are usually handled by dividing the signal into long fixed-length windows—long enough for the longest patterns to be recognized. We demonstrate a technique for recognizing acoustic patterns with a network that runs continuously in time and is fed a single spectral frame of input per network cycle. Behavior of the network in time is controlled by temporal regularities in the input patterns that allow the network to predict future events.
