Patent ID: 7343284

Claim:
A method for discriminating noise from signal in a noise-contaminated signal, comprising: decomposing a frame of the noise-contaminated signal received in a predefined time period into decorrelated signal components; for each component: i) recursively updating respective parameters characterizing a Gaussian noise distribution and a signal distribution of the component as a function of time; ii) using the respective parameters to evaluate a composite Gaussian and signal distribution function to provide an estimate of noise and signal contributions to the component; and attenuating the component in proportion to the estimated noise contribution to the component; wherein the signal is a noise-contaminated voice signal and recursively updating comprises recursively updating respective parameters characterizing the Gaussian noise distribution and a Laplacian voice distribution; wherein recursively updating respective parameters comprises using a value computed during processing of a previous frame to select which of the parameters characterizing each distribution to update; wherein the value computed during processing of a previous frame is an a priori probability that the frame constitutes noise, and using the a priori probability to select which of the parameters to update comprises: i) selecting a measure of variance that characterizes the Gaussian noise distribution if the a priori probability is below a predetermined threshold; and ii) otherwise selecting a measure of variance factor that characterizes the Laplacian distribution; wherein the a priori probability is defined by evaluating a hidden state of a hidden Markov model; and wherein recursively updating a parameter further comprises incrementally changing the parameter in accordance with a difference between an expected value of the component given the past value of the parameter, and the value of the component received; and wherein incrementally changing the parameter comprises applying a first order smoothing filter to the components.