Claims
- 1. A noise reduction system, comprising:a first spectral subtraction processor configured to filter a first signal to provide a first noise reduced output signal, wherein an amount of subtraction performed by the first spectral subtraction processor is controlled by a first subtraction factor, k1; a second spectral subtraction processor configured to filter a second signal to provide a noise estimate output signal, wherein an amount of subtraction performed by the second spectral subtraction processor is controlled by a second subtraction factor, k2; a third spectral subtraction processor configured to filter said first signal as a function of said noise estimate output signal, wherein an amount of subtraction performed by the third spectral subtraction processor is controlled by a third subtraction factor, k3; and a controller for dynamically determining at least one of the subtraction factors k1, k2, and k3 during operation of the noise reduction system.
- 2. The noise reduction system of claim 1, wherein the controller estimates a correlation between the first signal and the second signal.
- 3. The noise reduction system of claim 2, wherein the controller derives at least one of the first, second, and third subtraction factors, k1, k2, and k3, based on the correlation between the first signal and the second signal.
- 4. The noise reduction system of claim 3, wherein at least one of the subtraction factors, k1, k2, and k3, is smoothed over time.
- 5. The noise reduction system of claim 2, wherein the controller estimates a set of correlation samples of the first signal and the second signal and computes a correlation measurement as a sum of squares of the set of correlation samples.
- 6. The noise reduction system of claim 5, wherein at least one of the subtraction factors, k1, k2, and k3, is derived from the correlation measurement of the set of correlation samples.
- 7. The noise reduction system of claim 6, wherein at least one of the subtraction factors, k1, k2, and k3, is smoothed over time.
- 8. The noise reduction system of claim 2, wherein the controller estimates a set of correlation samples of the first signal and the second signal and computes a correlation measurement as a sum of an even function of the set of correlation samples.
- 9. The noise reduction system of claim 8, wherein at least one of the subtraction factors, k1, k2, and k3, is derived from the correlation measurement of the set of correlation samples.
- 10. The noise reduction system of claim 9, wherein at least one of the subtraction factors, k1, k2, and k3, is smoothed over time.
- 11. The noise reduction system of claim 2, wherein the subtraction factors k1, k2, and k3 are derived ask1(i)=(1−{overscore (γ)}(i))·t1+r1 k2(i)={overscore (γ)}(i)·t2+r2 k3(i)=(1−{overscore (γ)}(i))·t3+r3 where t1, t2, and t3 are scalar multiplication factors, r1, r2, and r3 are additive factors, and {overscore (γ)}(i) is an averaged square correlation sum of the first signal and the second signal.
- 12. The noise reduction system of claim 1, wherein the controller substantially equalizes energy levels of the first signal and the second signal.
- 13. The noise reduction system of claim 1, wherein the controller substantially equalizes magnitude levels of the first signal and the second signal.
- 14. The noise reduction system of claim 1, wherein the controller derives at least one of the first, second, and third subtraction factors k1, k2, and k3 from a ratio of a noise signal measurement of the first signal and a noise signal measurement of the second signal.
- 15. The noise reduction system of claim 14, wherein each of the noise signal measurements is an energy measurement.
- 16. The noise reduction system of claim 14, wherein each of the noise signal measurements is a magnitude measurement.
- 17. The noise reduction system of claim 14, wherein the controller computes at least one of a first relative positive measurement based on a first gain function and a second relative positive measurement based on a second gain function.
- 18. The noise reduction system of claim 17, wherein the noise signal measurement is derived from at least one of the first signal and the second signal and at least one of the first relative positive measurement and the second relative positive measurement, respectively.
- 19. The noise reduction system of claim 14, wherein a frequency dependent weighting function, performed by at least one of the first and second spectral subtraction processors, is used to derive at least one of a first and second frequency dependent positive measurement.
- 20. The noise reduction system of claim 19, wherein the noise signal measurement is derived from at least one of the first signal and the second signal and at least one of the first frequency dependent positive measurement and the second frequency dependent positive measurement.
- 21. The noise reduction system of claim 14, wherein the subtraction factors k1, k2, and k3 are derived as: k1 (i)=p1,x (i) (1-g_1,M (i-1))p2,x (i) g_2,M (i-1)·t1k2 (i)=p2,x (i) (1-g_2,M (i-1))p1,x (i) g_1,M (i)·t2.k3 (f,i)=p1,x (f,i) (1 G1,M (f,i))p2,x (f,i) G2,M (f,i)·t3,whereg_1,M (i)=1M ∑m-0M 1 G1,M (m,i),g_2,M (i)=1M ∑m-0M 1 G2,M (m,i),where p1,x(i) is an energy level of the first signal and p2,x(i) is an energy level of the second signal, t1, t2, and t3 are scalar multiplication factors, G1 is a first gain function, and G2 is a second gain function.
- 22. The noise reduction system of claim 1, wherein the controller derives at least one of the first, second, and third subtraction factors k1, k2, and k3 from a ratio of a desired signal measurement of the second signal and a desired signal measurement of the first signal.
- 23. The noise reduction system of claim 22, wherein each of the desired signal measurements is an energy measurement.
- 24. The noise reduction system of claim 22, wherein each of the desired signal measurements is a magnitude measurement.
- 25. The noise reduction system of claim 22, wherein the desired signal measurement is a speech signal measurement.
- 26. The noise reduction system of claim 22, wherein the controller computes at least one of a first relative positive measurement based on a first gain function and a second relative positive measurement based on a second gain function.
- 27. The noise reduction system of claim 26, wherein the desired signal measurement is derived from at least one of the first signal and the second signal and at least one of the first relative positive measurement and the second relative positive measurement, respectively.
- 28. The noise reduction system of claim 22, wherein a frequency dependent weighting function, performed by at least one of the first and second spectral subtraction processors, is used to derive at least one of a first and second frequency dependent positive measurement.
- 29. The noise reduction system of claim 28, wherein the desired signal measurement is derived from at least one of the first signal and the second signal and at least one of the first frequency dependent positive measurement and the second frequency dependent positive measurement.
- 30. The noise reduction system of claim 22, wherein the subtraction factors k1, k2, and k3 are derived as: k1 (i)=p1,x (i) (1-g_1,M (i-1))p2,x (i) g_2,M (i-1)·t1k2 (i)=p2,x (i) (1-g_2,M (i-1))p1,x (i) g_1,M (i)·t2.k3 (f,i)=p1,x (f,i) (1 G1,M (f,i))p2,x (f,i) G2,M (f,i)·t3,whereg_1,M (i)=1M ∑m-0M 1 G1,M (m,i),g_2,M (i)=1M ∑m-0M 1 G2,M (m,i),where p1,x(i) is a magnitude level of the first signal and p2,x(i) is a magnitude level of the second signal, t1, t2, and t3 are scalar multiplication factors, G1 is a first gain function, and G2 is a second gain function.
- 31. A method for processing a noisy input signal and a noise signal to provide a noise reduced output signal, comprising the steps of:(a) using spectral subtraction to filter said noisy input signal to provide a first noise reduced output signal, wherein an amount of subtraction performed is controlled by a first subtraction factor, k1; (b) using spectral subtraction to filter said noise signal to provide a noise estimate output signal, wherein an amount of subtraction performed is controlled by a second subtraction factor, k2; and (c) using spectral subtraction to filter said noisy input signal as a function of said noise estimate output signal, wherein an amount of subtraction performed is controlled by a third subtraction factor, k3, wherein at least one of the first, second, and third subtraction factors is dynamically determined during the processing of the noisy input signal and the noise signal.
- 32. The method of claim 31, wherein a correlation between the noisy input signal and the noise signal is estimated.
- 33. The method of claim 32, wherein at least one of the first, second, and third subtraction factors, k1, k2, and k3, is based on the correlation between the noisy input signal and the noise signal.
- 34. The method of claim 33, wherein at least one of the subtraction factors, k1, k2, and k3, is smoothed over time.
- 35. The method of claim 32, wherein a set of correlation samples of the noisy input signal and the noise signal are estimated and a correlation measurement as a sum of squares of the set of correlation samples is computed.
- 36. The method of claim 35, wherein at least one of the subtraction factors, k1, k2, and k3, is derived from the correlation measurement of the set of correlation samples.
- 37. The method of claim 36, wherein at least one of the subtraction factors, k1, k2, and k3, is smoothed over time.
- 38. The method of claim 32, wherein a set of correlation samples of the noisy input signal and the noise signal are estimated and a correlation measurement as a sum of an even function of the set of correlation samples is computed.
- 39. The method of claim 38, wherein at least one of the subtraction factors, k1, k2, and k3, is derived from the correlation measurement of the set of correlation samples.
- 40. The method of claim 39, wherein at least one of the subtraction factions, k1, k2, k3, is smoothed over time.
- 41. The method of claim 32, wherein the subtraction factors k1, k2, and k3 are derived ask1(i)=(1−{overscore (γ)}(i))·t1+r1 k2(i)={overscore (γ)}(i)·t2+r2 k3(i)=(1−{overscore (γ)}(i))·t3+r3 where t1, t2, and t3 are scalar multiplication factors, r1, r2, and r3 are additive factors, and {overscore (γ)}(i) is an averaged squared correlation sum of the noisy input signal and the noise signal.
- 42. The method of claim 31, wherein energy levels of the noisy input signal and the noise signal are substantially equalized.
- 43. The method of claim 31, wherein magnitude levels of the noisy input signal and the noise signal are substantially equalized.
- 44. The method of claim 31, wherein at least one of the first, second, and third subtraction factors k1, k2, and k3 is derived from a ratio of a noise signal measurement of the noisy input signal and a noise signal measurement of the noise signal.
- 45. The method of claim 44, wherein each of the noise signal measurements is an energy measurement.
- 46. The method of claim 44, wherein each of the noise signal measurements is a magnitude measurement.
- 47. The method of claim 44, wherein at least one of a first relative positive measurement based on a first gain function and a second relative positive measurement based on a second gain function is computed.
- 48. The method of claim 47, wherein the noise signal measurement is derived from at least one of the noisy input signal and the noise signal and at least one of the first relative positive measurement and the second relative positive measurement, respectively.
- 49. The method of claim 44, wherein a frequency dependent weighting function is used to derive at least one of a first and second frequency dependent positive measurement.
- 50. The method of claim 49, wherein the noise signal measurement is derived from at least one of the noisy input signal and the noise signal and at least one of the first frequency dependent positive measurement and the second frequency dependent positive measurement.
- 51. The method of claim 44, wherein the subtraction factors k1, k2, and k3 are derived as: k1 (i)=p1,x (i) (1-g_1,M (i-1))p2,x (i) g_2,M (i-1)·t1k2 (i)=p2,x (i) (1-g_2,M (i-1))p1,x (i) g_1,M (i)·t2,k3 (f,i)=p1,x (f,i) (1-G1,M (f,i))p2,x (f,i) G2,M (f,i)·t3,whereg_1,M (i)=1M ∑m=0M 1 G1,M (m,i),g_2,M (i)=1M ∑m=0M 1 G2,M (m,i),where p1,x(i) is an energy level of the noisy input signal and p2,x(i) is an energy level of the noise signal, t1, t2, and t3 are scalar multiplication factors, G1 is a first gain function and G2 is a second gain function.
- 52. The method of claim 31, wherein at least one of the first, second, and third subtraction factors k1, k2, and k3 is derived from a ratio of a desired signal measurement of the noise signal and a desired signal measurement of the noisy input signal.
- 53. The method of claim 52, wherein each of the desired signal measurements is an energy measurement.
- 54. The method of claim 52, wherein each of the desired signal measurements is a magnitude measurement.
- 55. The method of claim 52, wherein the desired signal is a speech signal.
- 56. The method of claim 52, wherein at least one of a first relative positive measurement based on a first gain function and a second relative positive measurement based on a second gain function is computed.
- 57. The method of claim 56, wherein the desired signal measurement is derived from at least one of the noisy input signal and the noise signal and at least one of the first relative positive measurement and the second relative positive measurement, respectively.
- 58. The method of claim 52, wherein a frequency dependent weighting function is used to derive at least one of a first and second frequency dependent positive measurement.
- 59. The method of claim 58, wherein the noise signal measurement is derived from at least one of the noisy input signal and the noise signal and at least one of the first frequency dependent positive measurement and the second frequency dependent positive measurement.
- 60. The method of claim 52, wherein the subtraction factors k1, k2, and k3 are derived as: k1 (i)=p1,x (i) (1-g_1,M (i-1))p2,x (i) g_2,M (i-1)·t1k2 (i)=p2,x (i) (1-g_2,M (i-1))p1,x (i) g_1,M (i)·t2,k3 (f,i)=p1,x (f,i) (1-G1,M (f,i))p2,x (f,i) G2,M (f,i)·t3,whereg_1,M (i)=1M ∑m=0M 1 G1,M (m,i),g_2,M (i)=1M ∑m=0M 1 G2,M (m,i),where p1,x(i) is a magnitude level of the noisy input signal and p2,x(i) is a magnitude level of the noise signal, t1, t2, and t3 are scalar multiplication factors, G1 is a first gain function and G2 is a second gain function.
CROSS REFERENCE TO RELATED APPLICATIONS
The present application is a continuation-in-part of U.S. patent application Ser. No. 09/289,065, filed on Apr. 12, 1999, now U.S. Pat. No. 6,549,586, and entitled “System and Method for Dual Microphone Signal Noise Reduction Using Spectral Subtraction,” which is a division of U.S. patent application Ser. No. 09/084,387, filed May 27, 1998, now U.S. Pat. No. 6,175,602, and entitled “Signal Noise Reduction by Spectral Subtraction using Linear Convolution and Causal Filtering,” which is a division of U.S. patent application Ser. No. 09/084,503, also filed May 27, 1998, now U.S. Pat. No. 6,459,914, and entitled “Signal Noise Reduction by Spectral Subtraction using Spectrum Dependent Exponential Gain Function Averaging.” Each of the above cited patent applications is incorporated herein by reference in its entirety.
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Continuation in Parts (1)
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