Bayesian suppression of memoryless nonlinear audio distortion

Even if nonlinear distortion may be deliberately applied to audio signals for esthetic or technical reasons, it is common to hear annoying defects in accidentally saturated or amateurishly processed audio - which calls for some means to automatically undo the impairment. This paper proposes an algorithm to blindly identify the nonlinear distortion suffered by an audio signal and reconstruct its original form. Designed to deal with memoryless impairments, the model adopted for the nonlinear distortion is a curve composed of an invertible sequence of linear segments, capable of following the typical shape of compressed audio, and whose parameters are easily interpretable and thus constrainable. The solution builds on the posterior statistical distribution of the curve parameters given the degraded signal, and yields perceptually impressive results for real signals distorted by arbitrary curves.