Method and system for speech processing for enhancement and detection

1. 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.

2. The method as claimed in claim 1 wherein a time constant of the first order smoothing filter is chosen as a time during which the distribution is stationary.

3. 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 using the respective parameters to determine which of the parameters to update comprises computing a measure of fit of the components to a composite Gaussian and Laplacian distribution;

wherein using the respective parameters to determine which of the parameters to update further comprises:

i) computing a measure of fit of each of the received components to a respective Gaussian noise distribution defined using the respective parameters; and

ii) comparing a mean of the measures of fit to the respective Gaussian noise distributions with a mean of the measures of fit to the composite Gaussian and Laplacian distributions, to compute a likelihood that the components of the frame constitute noise or noise-contaminated voice signal;

wherein computing a measure of fit to either of the distributions comprises evaluating the distribution at the value of the component received; and

wherein comparing a mean of the measures of fit comprises dividing a product of the measures of fit of the components to the composite Gaussian and Laplacian distribution by a product of the measures of fit of the components to the noise distribution.

4. The method as claimed in claim 3 wherein using the respective parameters to evaluate further comprises using the likelihood and the a priori probability to compute an a posteriori probability that the frame is noise-contaminated voice signal.

5. The method as claimed in claim 4 wherein using the respective parameters to evaluate further comprises using the a posteriori probability and a predefined fixed set of transition probabilities to compute an a priori probability that a next frame constitutes noise-contaminated voice signal.

6. 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 using the respective parameters to evaluate a composite Gaussian and signal distribution function comprises computing at least an approximation to an expected value of the composite Gaussian and signal distribution using a respective value of each component, and the parameters, to obtain a corresponding signal-enhanced component, if it is determined that the frame is signal active; and

wherein computing at least an approximation comprises computing a piece-wise function approximation of the expected value as a function of the parameters and the component.