Deciding the appropriate representation to use for modeling human auditory processing is a critical issue in auditory science. While engi(cid:173) neers have successfully performed many single-speaker tasks with LPC and spectrogram methods, more difficult problems will need a richer representation. This paper describes a powerful auditory representation known as the correlogram and shows how this non-linear representation can be converted back into sound, with no loss of perceptually impor(cid:173) tant information. The correlogram is interesting because it is a neuro(cid:173) physiologically plausible representation of sound. This paper shows improved methods for spectrogram inversion (conventional pattern playback), inversion of a cochlear model, and inversion of the correlo(cid:173) gram representation.