Noise filtering utilizing non-Gaussian signal statistics
The present invention is directed to a method and system for capturing an information signal from within a noisy background utilizing a non-Gaussian model for the a priori statistics of the information signal conditioned on other a priori quantities. A specific implementation utilizing a Gaussian Mixture Model (GMM) is described. The GMM implementation includes Wiener filtering as a special case, and includes methods for adaptively tracking multiple properties of the input noise and the information signal, including noise PSD, information signal PSD, information signal spectral amplitude, and probability of information signal presence versus time and frequency.
1. A method of extracting an information signal from input signal containing both the information signal and noise, including the steps of:
decomposing the input signal into multiple spectral bands utilizing Fourier transforms;
estimating a non-Gaussian distribution function model for the information signal spectral amplitude;
dynamically updating said non-Gaussian distribution function model for said information signal spectral amplitude;
producing a gain function for each of said spectral bands utilizing said dynamically undated non-Gaussian distribution function for said information signal spectral amplitude;
applying said gain function for each of said spectral bands to the input signal spectral bands to produce estimated information signal components for each of said spectral bands; and
combining said estimated information signal components for all of said spectral bands to produce an estimate of the information signal with reduced noise.
2. The method in accordance with claim 1 , wherein said non-Gaussian distribution function model for the information signal is a Gaussian Mixture Model.
3. The method in accordance with claim 1 , further including the step of estimating current information signal power.
4. The method in accordance with claim 1 , further including the step of estimating current noise power.
5. The method in accordance with claim 4 , further including the step of estimating current information signal power.
6. The method in accordance with claim 5 , wherein said non-Gaussian distribution function model for the information signal is a Gaussian Mixture Model.
7. The method in accordance with claim 5 , further including the step of estimating current probability of information signal presence.
8. The method in accordance with claim 7 , wherein said non-Gaussian distribution function model for the information signal is a Gaussian Mixture Model.
9. The method in accordance with claim 1 , further including the steps of:
estimating current information signal power based upon input signal power, prior information signal power, noise power, and probability of information signal presence;
estimating current noise power based upon input signal power, information signal power, prior noise power, and probability of information signal presence; and
estimating current probability of information signal presence based upon input signal power, information signal power, noise power, and prior probability of information signal presence.
10. The method in accordance with claim 9 , wherein said non-Gaussian distribution function model for the information signal is a Gaussian Mixture Model.
11. A system for extracting an information signal from an input signal containing both the information signal and noise, comprising:
means for estimating a non-Gaussian distribution function model for the information signal spectral amplitude;
means for decomposing the input signal into multiple spectral bands utilizing Fourier transforms;
means for dynamically updating said non-Gaussian distribution function model for said information signal spectral amplitude;
means for producing a gain function for each of said spectral bands utilizing said dynamically undated non-Gaussian distribution function for said information signal spectral amplitude;
means for applying said gain function for each of said spectral bands to the input signal spectral bands to produce estimated information signal components for each of said spectral bands; and
means for combining said estimated information signal components for all of said spectral bands to produce an estimate of the information signal with reduced noise.
12. The system in accordance with claim 11 , further including means for producing current information signal power for each of said spectral bands based upon input signal power, prior information signal power, noise power and probability of information signal presence in the input signal.
13. The system in accordance with claim 12 , further including means for producing current noise power for each of said spectral band, based upon input signal power, information signal power, prior noise power and probability of information signal presence in the input signal.
14. The system in accordance with claim 13 , further including means for producing current probability of information signal presence for each of said spectral bands based upon input signal power, information signal power, noise power and prior probability of information signal presence in the input signal.
15. The system in accordance with claim 14 , wherein said non-Gaussian distribution function model for the information signal is a Gaussian Mixture Model.