Method of noise reduction using instantaneous signal-to-noise ratio as the principal quantity for optimal estimation

• US 20050043945A1
• Filed: 08/19/2003
• Published: 02/24/2005
• Est. Priority Date: 08/19/2003
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Deng, Li, Droppo, James G., Acero, Alejandro
• Granted Patent: US 7,363,221 B2
• Time in Patent Office: Days
• US Class Current: 704/226
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

A system and method are provided that accurately estimate noise and that reduce noise in pattern recognition signals. The method and system define a mapping random variable as a function of at least a clean signal random variable and a noise random variable. A model parameter that describes at least one aspect of a distribution of values for the mapping random variable is then determined. Based on the model parameter, an estimate for the clean signal random variable is determined. Under many aspects of the present invention, the mapping random variable is a signal-to-noise ratio variable and the method and system estimate a value for the signal-to-noise ratio variable from the model parameter.

Claims

1. A method of identifying an estimate for a clean signal random variable representing a portion of a clean signal found within a noisy signal, the method comprising:

defining a mapping random variable as a function of at least the clean signal random variable and a noise random variable;

determining a model parameter that describes at least one aspect of a distribution of values for the mapping random variable; and

using the model parameter to determine an estimate for the clean signal random variable from an observed value.

2. The method of claim 1 wherein defining the mapping random variable as a function of at least the clean signal random variable and the noise random variable comprises defining the mapping variable as a ratio of the clean signal random variable to the noise random variable.

3. The method of claim 2 wherein determining a model parameter comprises determining a mean of the mapping random variable.

4. The method of claim 1 further comprising using the model parameter to determine an estimate of the mapping random variable.

5. The method of claim 4 wherein defining the mapping random variable as a function of at least the clean signal random variable and the noise random variable comprises defining the mapping variable as a ratio of the clean signal random variable to the noise random variable.

6. The method of claim 1 wherein determining a model parameter comprises approximating a function of the mapping random variable using a Taylor series expansion.

7. The method of claim 6 further comprising performing an iteration comprising steps of:

calculating a mean for the mapping random variable using a Taylor series expansion;

setting a new expansion point for the Taylor series expansion equal to the mean of the mapping random variable; and

repeating the iteration steps using the new expansion point.

8. The method of claim 1 further comprising:

determining a clean signal model parameter that describes at least one aspect of a distribution of values for the clean signal random variable; and

using the clean signal model parameter to determine the estimate for the clean signal random variable.

9. The method of claim 8 further comprising:

determining a noise model parameter that describes at least one aspect of a distribution of values for the noise random variable; and

using the noise model parameter to determine the estimate for the clean signal random variable.

10. The method of claim 9 wherein determining the noise model parameter comprises determining the noise model parameter from noise estimates collected from the noisy signal.

11. A computer-readable medium having computer-executable instructions for performing steps comprising:

defining a random variable as a function of a signal-to-noise ratio variable;

determining distribution parameters for the signal-to-noise ratio based on the defined function; and

using the distribution parameters to determine an estimate of the signal-to-noise ratio.

12. The computer-readable medium of claim 11 wherein the random variable comprises a clean signal random variable representing a portion of a clean signal.

13. The computer-readable medium of claim 11 wherein the random variable comprises a noise signal random variable representing a noise in an observed signal.

14. The computer-readable medium of claim 11 wherein defining a random variable further comprises defining the random variable as a function of an observed value.

15. The computer-readable medium of claim 11 wherein determining a distribution parameter further comprises approximating at least a portion of the defined function with an approximation function.

16. The computer-readable medium of claim 15 wherein the approximation function comprises a Taylor series approximation.

17. The computer-readable medium of claim 16 wherein determining a distribution parameter further comprises performing an iteration.

18. The computer-readable medium of claim 17 wherein performing an iteration comprises performing steps of:

using the Taylor series approximation to determine a mean for the signal-to-noise ratio;

setting a new expansion point equal to the mean for the signal-to-noise ratio; and

repeating the step of using the Taylor series approximation to determine a mean while using the new expansion point.

19. The computer-readable medium of claim 11 further comprising using the distribution parameter to determine an estimate of the random variable.

20. The computer-readable medium of claim 19 wherein the random variable is a clean signal random variable representing a portion of a clean signal.

21. The computer-readable medium of claim 11 wherein determining a distribution parameter further comprises determining the distribution parameter based on a model parameter that describes a distribution of clean signal values, each clean signal value representing a portion of a clean signal.

22. The computer-readable medium of claim 11 wherein determining a distribution parameter further comprises determining the distribution parameter based on a model parameter that describes a distribution of noise values.

23. The computer-readable medium of claim 22 further comprising determining the model parameter from an observed signal.