Claims
- 1. A method for improving a linear predictive analysis procedure for a G.729 standard to create an improved G.729 standard, wherein the linear predictive analysis procedure comprises windowing a preprocessed speech signal with a G.729 window, comprising:
replacing the window with an improved G.729 window; and using the improved G.729 window to window the preprocessed speech signal.
- 2. The method, as claimed in claim 1, wherein the optimized window comprises an optimized window created by an alternate window optimization procedure.
- 3. The method, as claimed in claim 2, wherein the optimized window comprises a window w1.
- 4. The method, as claimed in claim 2, wherein the optimized window comprises a window w2.
- 5. The method, as claimed in claim 2, wherein the optimized window comprises a window w3.
- 6. The method, as claimed in claim 5, wherein the G.729 standard further comprises a future buffering requirement of 40 samples; and wherein the method further comprises reducing the future buffering requirement to approximately 20 samples.
- 7. The method, as claimed in claim 2, wherein the optimized window comprises a window w4.
- 8. The method, as claimed in claim 7, wherein the G.729 standard further comprises a future buffering requirement of 40 samples; and wherein the method further comprises reducing the future buffering requirement to approximately zero samples.
- 9. The method, as claimed in claim 2, wherein the optimized window comprises a window w5.
- 10. The method, as claimed in claim 9, wherein the G.729 standard further comprises a future buffering requirement of 40 samples; and wherein the method further comprises reducing the future buffering requirement to approximately 20 samples.
- 11. The method, as claimed in claim 1, wherein the G.729 standard further comprises an LSP interpolation factor and the method further comprising replacing the LSP interpolation factor with an optimized LSP interpolation factor.
- 12. The method, as claimed in claim 11, wherein the optimized window comprises a window w6 and the optimized LSP interpolation factor is 0.88.
- 13. The method, as claimed in claim 12, wherein the G.729 standard further comprises a future buffering requirement of 40 samples; and wherein the method further comprises reducing the future buffering requirement to approximately 20 samples.
- 14. The method, as claimed in claim 11, wherein the optimzed window comprises a window w7 and the optimized LSP interpolation factor is 0.96.
- 15. The method, as claimed in claim 14, wherein the G.729 standard further comprises a future buffering requirement of 40 samples; and wherein the method further comprises reducing the future buffering requirement to approximately 10 samples.
- 16. The method, as claimed in claim 11, wherein the optimized window comprises a window w8 and the optimized LSP interpolation factor is 1.03.
- 17. The method, as claimed in claim 16, wherein the G.729 standard further comprises a future buffering requirement of 40 samples; and wherein the method further comprises reducing the future buffering requirement to approximately zero samples.
- 18. A method for improving a linear predictive analysis procedure for a G.729 standard to create an improved G.729 standard, wherein the linear predictive analysis procedure interpolates a plurality of quantized LSP coefficients using an LSP interpolation factor, comprising:
replacing the LSP interpolation factor with an optimized LSP interpolation factor; and using the optimized LSP interpolation factor to interpolate the plurality of quantized LSP coefficients.
- 19. The method, as claimed in claim 18, wherein the optimized LSP interpolation factor comprises an optimized LSP interpolation factor created by an LSP interpolation factor optimization procedure.
- 20. The method, as claimed in claim 19, wherein the optimized LSP interpolation factor is 0.88.
- 21. The method, as claimed in claim 19, wherein the optimized LSP interpolation factor is 0.96.
- 22. The method, as claimed in claim 19, wherein the optimized LSP interpolation factor is 1.03.
- 23. An improved linear prediction analysis procedure for a G.729 standard, comprising:
(A) high pass filtering and scaling a speech signal to create a preprocessed speech signal, wherein the preprocessed speech signal comprises a plurality of segments; (B) windowing the preprocessed speech signal with an optimized G.729 window to create a frame of the preprocessed speech signal, wherein the frame comprises one of the plurality of segments of the preprocessed speech signal and includes a first subframe and a second subframe; (C) determining a plurality of optimized unquantized LP coefficients for the frame through autocorrelation; (D) transforming the plurality of optimized unquantized LP coefficients for the frame into optimized LSP coefficients for the second subframe of the frame; (E) quantizing the plurality of optimized LSP coefficients for the second frame of the frame to create quantized optimized LSP coefficients for the second frame of the frame; (F) interpolating the quantized optimized LSP coefficients for the second frame of the frame with optimized LSP coefficients for a second frame of a prior frame to create quantized optimized LSP coefficients of the first subframe of the frame; (G) transforming the quantized optimized LSP coefficients of the first and second subframes of the frame into quantized optimized LP coefficients of the first and second subframes, respectively; and (H) repeating steps (B) through (G) for each of the plurality of segments of the preprocessed speech signal.
- 24. The improved linear prediction analysis procedure, as claimed in claim 23, wherein the optimized G.729 window comprises an optimized G.729 window created by an alternate window optimization procedure.
- 25. The improved linear prediction analysis procedure, as claimed in claim 23, wherein the optimized G.729 window comprises a window w1.
- 26. The improved linear prediction analysis procedure, as claimed in claim 23, wherein the optimized G.729 window comprises a window w2.
- 27. The improved linear prediction analysis procedure, as claimed in claim 23, wherein the optimized G.729 window comprises a window w3.
- 28. The improved linear prediction analysis procedure, as claimed in claim 27, wherein the improved linear prediction analysis further comprises a future buffering requirement of approximately 20 samples.
- 29. The improved linear prediction analysis procedure, as claimed in claim 23, wherein the optimized G.729 window comprises a window w4.
- 30. The improved linear prediction analysis procedure, as claimed in claim 27, wherein the improved linear prediction analysis further comprises a future buffering requirement of approximately zero samples.
- 31. The improved linear prediction analysis procedure, as claimed in claim 23, wherein the optimized G.729 window comprises a window w5.
- 32. The improved linear prediction analysis procedure, as claimed in claim 31, wherein the improved linear prediction analysis further comprises a future buffering requirement of approximately 20 samples.
- 33. The improved linear prediction analysis procedure, as claimed in claim 23, wherein interpolating the quantized optimized LSP coefficients comprises interpolating the quantized optimized LSP coefficients with an optimized LSP interpolation factor.
- 34. The improved linear prediction analysis procedure, as claimed in claim 33, wherein the optimized G.729 window comprises a window w6 and the optimized LSP interpolation factor is 0.88.
- 35. The improved linear prediction analysis procedure, as claimed in claim 34, wherein the improved linear prediction analysis further comprises a future buffering requirement of approximately 20 samples.
- 36. The improved linear prediction analysis procedure, as claimed in claim 33, wherein the optimized G.729 window comprises a window w7 and the optimized LSP interpolation factor is 0.96.
- 37. The improved linear prediction analysis procedure, as claimed in claim 36, wherein the improved linear prediction analysis further comprises a future buffering requirement of approximately 10 samples.
- 38. The improved linear prediction analysis procedure, as claimed in claim 33, wherein the optimized G.729 window comprises a window w8 and the optimized LSP interpolation factor is 1.03.
- 39. The improved linear prediction analysis procedure, as claimed in claim 38, wherein the improved linear prediction analysis further comprises a future buffering requirement of approximately zero samples.
- 40. An improved G.729 standard, comprising:
the steps of claims 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, or 39; and determining an excitation signal for the frame using the quantized optimized LP coefficients of the first and second subframes of the frame for each of the plurality of segments of the preprocessed speech signal.
- 41. An alternate window optimization procedure for optimizing a window used to isolate a speech signal into a plurality of frames, comprising:
(A) assuming a window; (B) determining a window prediction error energy; wherein the window prediction error energy is a prediction error energy associated with the window; (C) estimating a gradient of the window prediction error energy; (D) updating the window in a direction negative to the gradient of the widow prediction error energy to create an updated window and to redefine the window with the updated window; (E) redetermining the window prediction error energy as a function of the window; (F) determining if a threshold has been reached, wherein if the threshold has not been reached, repeating steps (C), (D), (E) and (F) until the threshold has been met.
- 42. The alternate window optimization procedure, as claimed in claim 41, wherein determining the window prediction error energy includes using an autocorrelation method.
- 43. The alternate window optimization procedure, as claimed in claim 41, wherein estimating the gradient of the window prediction error energy includes:
defining an intermediate window; determining an intermediate prediction error energy; wherein the intermediate prediction error energy is a prediction error energy associated with the intermediate window; and estimating the gradient of the window prediction error energy as a function of the window prediction error energy and the intermediate prediction error energy.
- 44. The alternate window optimization procedure, as claimed in claim 43, wherein defining the intermediate window includes defining a plurality of intermediate window samples w′[n] as a function of a first sample index n, a second sample index no, a plurality of window samples w[n], a window perturbation constant Δw, and according to equations w′[n]=w[n], n≠no; w′[no]=w[no]+Δw, n=no.
- 45. The alternate window optimization procedure, as claimed in claim 44, wherein the window perturbation constant Δw equals from approximately 10−7 to approximately 10−4.
- 46. The alternate window optimization procedure, as claimed in claim 44, wherein estimating the gradient of the window prediction error energy includes estimating a derivative of the window prediction error energy with respect to each of the plurality of window samples according to a basic definition of a derivative.
- 47. The alternate window optimization procedure, as claimed in claim 46, wherein estimating the derivative of the window prediction error energy with respect to each of the plurality of window samples according to the basic definition of the derivative includes estimating the derivative of the window prediction error energy with respect to each of the plurality of window samples
- 48. The alternate window optimization procedure, as claimed in claim 41, wherein updating the window in the direction negative to the gradient of the widow prediction error energy to create an updated window and to redefine the window with the updated window includes, creating a sample of the updated window wm[n]updated as a function of a sample index n, a window sample wm[n], a derivative of the window prediction error energy with respect to the window sample
- 49. The alternate window optimization procedure, as claimed in claim 48, wherein the step size parameter μ is equal to approximately 10−9.
- 50. The alternate window optimization procedure, as claimed in claim 41, wherein redetermining the window prediction error energy as a function of the s window includes determining the window prediction error energy as a function of the window using an autocorrelation method.
- 51. The alternate window optimization procedure, as claimed in claim 41, wherein assuming a window includes assuming a G.729 window.
- 52. The alternate window optimization procedure, as claimed in claim 41, wherein assuming a window includes assuming a rectangular window.
- 53. An LSP interpolation factor optimization procedure for optimizing an LSP interpolation factor, comprising:
(A) assigning an initial value to an LSP interpolation factor; (B) determining a first SPG, wherein the first SPG is an SPG associated with the LSP interpolation factor; (C) defining a new LSP interpolation factor by incrementing the LSP interpolation factor by a fixed step size in an incrementation direction; (D) determining a second SPG, wherein the second SPG is an SPG associated with the new LSP interpolation factor; (E) determining whether the second SPG is larger than or approximately equal to the first SPG;
wherein if the second SPG is not larger than or approximately equal to the first SPG, repeating determining whether the incrementation direction has been previously reversed or the LSP interpolation factor has been previously updated, reversing the incrementation direction, redefining the new LSP interpolation factor, redetermining the second SPG, and determining whether the second SPG is larger than or approximately equal to the first SPG, until the second SPG is larger than or approximately equal to the first SPG; wherein if the second SPG is larger than or approximately equal to the first SPG, updating the LSP interpolation factor to equal the new LSP interpolation factor and determining whether a stop criterion has been met; wherein if the stop criterion has not been met, repeating steps (C), (D) and (E) until the stop criterion has been met.
- 54. An LSP interpolation factor optimization procedure, as claimed in claim 53, wherein the initial value is approximately 0.5.
- 55. An LSP interpolation factor optimization procedure, as claimed in claim 53, wherein the fixed step size is approximately 0.01.
- 56. A joint window and LSP interpolation factor optimization procedure, comprising:
(A) optimizing a window using a window optimization procedure; (B) adjusting a current LSP interpolation factor to create an adjusted LSP interpolation factor; and (C) determining whether a stop criterion has been met, wherein if the stop criterion has not been met, repeating steps (A), (B) and (C) until the stop criterion has been met.
- 57. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 56, wherein the window optimization procedure is a primary window optimization procedure.
- 58. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 56, wherein the window optimization procedure is an alternate window optimization procedure.
- 59. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 58, wherein the alternate window optimization procedure comprises:
(A) assuming a window; (B) determining a window prediction error energy; wherein the window prediction error energy is a prediction error energy associated with the window; (C) estimating a gradient of the window prediction error energy; (D) updating the window in a direction negative to the gradient of the widow prediction error energy to create an updated window and to redefine the window with the updated window; (E) redetermining the window prediction error energy as a function of the window; (F) determining if a threshold has been reached, wherein if the threshold has not been reached, repeating steps (C), (D), (E) and (F) until the threshold has been met.
- 60. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 59, wherein determining the window prediction error energy includes using an autocorrelation method.
- 61. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 59, wherein estimating the gradient of the prediction error energy associated with the perturbed window includes:
defining an intermediate window; determining an intermediate prediction error energy; wherein the intermediate prediction error energy is a prediction error energy associated with the intermediate window; and estimating the gradient of the window prediction error energy as a function of the window prediction error energy and the intermediate prediction error energy.
- 62. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 61, wherein defining the intermediate window includes defining a plurality of intermediate window samples w′[n] as a function of a first sample index n, a second sample index no, a plurality of window samples w[n], a window perturbation constant Δw, and according to equations w′[n]=w[n], n≠no; w′[no]=w[no]+Δw, n=no.
- 63. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 64, wherein the window perturbation constant Δw equals from approximately 10−7 to approximately 10−4.
- 64. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 61, wherein estimating the gradient of the window prediction error energy includes estimating a derivative of the window prediction error energy with respect to each of the plurality of window samples according to a basic definition of a derivative.
- 65. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 64, wherein estimating the derivative of the window prediction error energy with respect to each of the plurality of window samples according to the basic definition of the derivative includes estimating the derivative of the window prediction error energy with respect to each of the plurality of window samples
- 66. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 64, wherein updating the window in the direction negative to the gradient of the widow prediction error energy to create an updated window and to redefine the window with the updated window includes, creating a sample of the updated window wm[n]updated as a function of a sample index n, a window sample wm[n], a derivative of the window prediction error energy with respect to the window sample
- 67. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 66, wherein the step size parameter μ is equal to approximately 10−9.
- 68. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 67, wherein redetermining the window prediction error energy as a function of the window includes determining the window prediction error energy as a function of the window using an autocorrelation method.
- 69. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 59, wherein assuming a window includes assuming a G.729 window.
- 70. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 59, wherein assuming a window includes assuming a rectangular window.
- 71. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 56, wherein adjusting a current LSP interpolation factor to create an adjusted LSP interpolation factor comprises:
determining a first SPG, wherein the first SPG is an SPG associated with the current LSP interpolation factor; defining a new LSP interpolation factor by incrementing the current LSP interpolation factor by a fixed step size in an incrementation direction; determining a second SPG, wherein the second SPG is an SPG associated with the new LSP interpolation factor; and determining if the second SPG is larger than or approximately equal to the first SPG; wherein if the second SPG is not larger than or approximately equal to the first SPG, determining whether the incrementation direction has been previously reversed or if the LSP interpolation factor had been previously updated; wherein if wherein if the incrementation direction has been previously reversed or if the LSP interpolation factor has been previously updated, resuming the joint window and LSP interpolation factor optimization procedure with step (C); wherein if the incrementation direction has not been previously reversed and if the LSP interpolation factor has not been previously updated, reversing the incrementation direction; and wherein if the second SPG is larger than or approximately equal to the first SPG updating the current LSP interpolation factor to equal the next LSP interpolation factor.
- 72. The method for jointly optimizing the window and the interpolation factor, as claimed in claim 56, wherein the fixed step size is approximately 0.01.
- 73. An optimized window for a G.729 speech coding standard comprising a plurality of samples and a plurality of values, wherein each of the plurality of samples n includes one of the plurality of values such that for n=[0,239] the plurality of values comprise w1.
- 74. An optimized window for a G.729 speech coding standard comprising, a first plurality of sample values wa, wherein the first plurality of sample values are approximately within a distance d=0.0001 of a window comprising a second plurality of sample values wb, wherein the second plurality of sample values wb comprises w1, and wherein the distance d between wa and wb is defined according to a number of samples N, a first index n, a second index k, and according to an equation:
- 75. An optimized window for a G.729 speech coding standard comprising a plurality of samples and a plurality of values, wherein each of the plurality of samples n includes one of the plurality of values such that for n=[0,159] the plurality of values comprise w2.
- 76. An optimized window for a G.729 speech coding standard comprising, a first plurality of sample values wa, wherein the first plurality of sample values are approximately within a distance d=0.0001 of a window comprising a second plurality of sample values wb, wherein the second plurality of sample values wb comprises w2, and wherein the distance d between wa and wb is defined according to a number of samples N, a first index n, a second index k, and according to an equation:
- 77. An optimized window for a G.729 speech coding standard comprising a plurality of samples and a plurality of values, wherein each of the plurality of samples n includes one of the plurality of values such that for n=[0,79] the plurality of values comprise w3.
- 78. An optimized window for a G.729 speech coding standard comprising, a first plurality of sample values wa, wherein the first plurality of sample values are approximately within a distance d=0.0001 of a window comprising a second plurality of sample values wb, wherein the second plurality of sample values wb comprises w3, and wherein the distance d between wa and wb is defined according to a number of samples N, a first index n, a second index k, and according to an equation:
- 79. An optimized window for a G.729 speech coding standard comprising a plurality of samples and a plurality of values, wherein each of the plurality of samples n includes one of the plurality of values such that for n=[0,119] the plurality of values comprise w4.
- 80. An optimized window for a G.729 speech coding standard comprising, a first plurality of sample values wa, wherein the first plurality of sample values are approximately within a distance d=0.0001 of a window comprising a second plurality of sample values wb, wherein the second plurality of sample values wb comprises w4, and wherein the distance d between wa and wb is defined according to a number of samples N, a first index n, a second index k, and according to an equation:
- 81. An optimized window for a G.729 speech coding standard comprising a plurality of samples and a plurality of values, wherein each of the plurality of samples n includes one of the plurality of values such that for n=[0,119] the plurality of values comprise w5.
- 82. An optimized window for a G.729 speech coding standard comprising, a first plurality of sample values wa, wherein the first plurality of sample values are approximately within a distance d=0.0001 of a window comprising a second plurality of sample values wb, wherein the second plurality of sample values wb comprises w5, and wherein the distance d between wa and wb is defined according to a number of samples N, a first index n, a second index k, and according to an equation:
- 83. An optimized window for a G.729 speech coding standard comprising a plurality of samples and a plurality of values, wherein each of the plurality of samples n includes one of the plurality of values such that for n=[0,119] the plurality of values comprise w6.
- 84. An optimized window for a G.729 speech coding standard comprising, a first plurality of sample values wa, wherein the first plurality of sample values are approximately within a distance d=0.0001 of a window comprising a second plurality of sample values wb, wherein the second plurality of sample values wb comprises w6, and wherein the distance d between wa and wb is defined according to a number of samples N, a first index n, a second index k, and according to an equation:
- 85. An optimized window for a G.729 speech coding standard comprising a plurality of samples and a plurality of values, wherein each of the plurality of samples n includes one of the plurality of values such that for n=[0,119] the plurality of values comprise w7.
- 86. An optimized window for a G.729 speech coding standard comprising, a first plurality of sample values wa, wherein the first plurality of sample values are approximately within a distance d=0.0001 of a window comprising a second plurality of sample values wb, wherein the second plurality of sample values wb comprises w7, and wherein the distance d between wa and wb is defined according to a number of samples N, a first index n, a second index k, and according to an equation:
- 87. An optimized window for a G.729 speech coding standard comprising a plurality of samples and a plurality of values, wherein each of the plurality of samples n includes one of the plurality of values such that for n=[0,119] the plurality of values comprise w8.
- 88. An optimized window for a G.729 speech coding standard comprising, a first plurality of sample values wa, wherein the first plurality of sample values are approximately within a distance d=0.0001 of a window comprising a second plurality of sample values wb, wherein the second plurality of sample values wb comprises w8, and wherein the distance d between wa and wb is defined according to a number of samples N, a first index n, a second index k, and according to an equation:
- 89. An optimized LSP interpolation factor for a G.729 speech coding standard wherein the optimized interpolation factor was created using an LSP interpolation factor optimization procedure and has a value of approximately 0.88.
- 90. An optimized LSP interpolation factor for a G.729 speech coding standard wherein the optimized interpolation factor was created using an LSP interpolation factor optimization procedure and has a value of approximately 0.96.
- 91. An optimized LSP interpolation factor for a G.729 speech coding standard wherein the optimized interpolation factor was created using an LSP interpolation factor optimization procedure and has a value of approximately 1.03.
- 92. A computer readable storage medium storing computer readable data comprising an optimized window for use with a linear predictive analysis procedure of an ITU-T G 729 standard, the optimized window comprising a first plurality of sample values, wherein the first plurality of sample values comprises: −0.000237, −0.000459, −0.000649, −0.000732, −0.000810, −0.000869, −0.000963, −0.001035, −0.001105, −0.001133, −0.001164, −0.001172, −0.001199, −0.001220, −0.001224, −0.001189, −0.001173, −0.001170, −0.001171, −0.001129, −0.001084, −0.001020 −0.000961, −0.000868, −0.000791, −0.000732, −0.000672, −0.000578, −0.000498, −0.000389, −0.000270, −0.000155, −0.000082, 0.000036, 0.000179, 0.000366, 0.000547, 0.000777, 0.000966, 0.001163, 0.001429, 0.001704, 0.002034, 0.002442, 0.002768, 0.003009, 0.003316, 0.003736, 0.004208, 0.004593, 0.005027, 0.005572, 0.006214, 0.006862, 0.007512, 0.008072, 0.008762, 0.009537, 0.010259, 0.010780, 0.011326, 0.012035, 0.012984, 0.014061, 0.015185, 0.016201, 0.017164, 0.018104, 0.019315, 0.020451, 0.021626, 0.022905, 0.024416, 0.025818, 0.027392, 0.029275, 0.031447, 0.033451, 0.035310, 0.037503, 0.040073, 0.042859, 0.045619, 0.048478, 0.051622, 0.055232, 0.058549, 0.062056, 0.066313, 0.071063, 0.075693, 0.079987, 0.084691, 0.089954, 0.095469, 0.101106, 0.106946, 0.113332, 0.119882, 0.127238, 0.134548, 0.141031, 0.149027, 0.158435, 0.168282, 0.178534, 0.188088, 0.197224, 0.207630, 0.218278, 0.229549, 0.242790, 0.257393, 0.272263, 0.287628, 0.302727, 0.320260, 0.338398, 0.356662, 0.375756, 0.391461, 0.402353, 0.411523, 0.426919, 0.442097, 0.457125, 0.470478, 0.482690, 0.493665, 0.505192, 0.515466, 0.524607, 0.535684, 0.547782, 0.559191, 0.567584, 0.575941, 0.586021, 0.594891, 0.603359, 0.610649, 0.621802, 0.635396, 0.648406, 0.658483, 0.670266, 0.681464, 0.690586, 0.701875, 0.713891, 0.726785, 0.742499, 0.759478, 0.774364, 0.788681, 0.804063, 0.821424, 0.841290, 0.859994, 0.872394, 0.887378, 0.904173, 0.918841, 0.927554, 0.934721, 0.942769, 0.951851, 0.957711, 0.964783, 0.971730, 0.977872, 0.980500, 0.982293, 0.985078, 0.993160, 0.995710, 0.997114, 0.998474, 1.000000, 0.997149, 0.997424, 0.993460, 0.989936, 0.988384, 0.988770, 0.985183, 0.984698, 0.982134, 0.978749, 0.969219, 0.961557, 0.952310, 0.946076, 0.934954, 0.924269, 0.910016, 0.896763, 0.878485, 0.855556, 0.829415, 0.806306, 0.785402, 0.770519, 0.760567, 0.747101, 0.730306, 0.713891, 0.696630, 0.680546, 0.665455, 0.650196, 0.633707, 0.618217, 0.605972, 0.592923, 0.578437, 0.563725, 0.551464, 0.538158, 0.519843, 0.500879, 0.486195, 0.472855, 0.458538, 0.440057, 0.422272, 0.402885, 0.383262, 0.361882, 0.338678, 0.316555, 0.298506, 0.279068, 0.255606, 0.227027, 0.201944, 0.174543, 0.143867, 0.096811, 0.044805.
- 93. A computer readable storage medium storing computer readable data comprising an optimized window for use with a linear predictive analysis procedure of an ITU-T G 729 standard, the optimized window comprising a first plurality of sample values, wherein the first plurality of sample values comprises: 0.005167, 0.011981, 0.017841, 0.022244, 0.026553, 0.031068, 0.035846, 0.040391, 0.045182, 0.050268, 0.055649, 0.061057, 0.066831, 0.072674, 0.078826, 0.085156, 0.091575, 0.098293, 0.105681, 0.113773, 0.121601, 0.129022, 0.138047, 0.148204, 0.158398, 0.169204, 0.179212, 0.188430, 0.198946, 0.210257, 0.222133, 0.236050, 0.251162, 0.266475, 0.282524, 0.298583, 0.315814, 0.334517, 0.352428, 0.372199, 0.388440, 0.400000, 0.408924, 0.424639, 0.440411, 0.455531, 0.469013, 0.481291, 0.492587, 0.504662, 0.514708, 0.524576, 0.535741, 0.547732, 0.558973, 0.567273, 0.575847, 0.585113, 0.594603, 0.603477, 0.610688, 0.621035, 0.635554, 0.648061, 0.658219, 0.669725, 0.681601, 0.691051, 0.702236, 0.713983, 0.726843, 0.742869, 0.760467, 0.776139, 0.790253, 0.805735, 0.822836, 0.842261, 0.861448, 0.874584, 0.888622, 0.905988, 0.920321, 0.929926, 0.935623, 0.943977, 0.953429, 0.959648, 0.965468, 0.973359, 0.978007, 0.981078, 0.982898, 0.985956, 0.993341, 0.996419, 0.997015, 0.998812, 1.000000, 0.997307, 0.997038, 0.993513, 0.990205, 0.988309, 0.987577, 0.984662, 0.984077, 0.981707, 0.978162, 0.968782, 0.960647, 0.952468, 0.945065, 0.934680, 0.923900, 0.908954, 0.894633, 0.878203, 0.854567, 0.828177, 0.804822, 0.783795, 0.768115, 0.758442, 0.745928, 0.728510, 0.712191, 0.694841, 0.679219, 0.663613, 0.647964, 0.631325, 0.616391, 0.603800, 0.590816, 0.575476, 0.561171, 0.549193, 0.535428, 0.516958, 0.497337, 0.482519, 0.469258, 0.454658, 0.436620, 0.419015, 0.399476, 0.379941, 0.357838, 0.335101, 0.313163, 0.295549, 0.276211, 0.253050, 0.224296, 0.199336, 0.172305, 0.141446, 0.095822, 0.043428.
- 94. A computer readable storage medium storing computer readable data comprising an optimized window for use with a linear predictive analysis procedure of an ITU-T G 729 standard, the optimized window comprising a first plurality of sample values, wherein the first plurality of sample values comprises: 0.070562, 0.153128, 0.223865, 0.277425, 0.328933, 0.378871, 0.428875, 0.466903, 0.502980, 0.540652, 0.577244, 0.609723, 0.642362, 0.674990, 0.707747, 0.736262, 0.760856, 0.788273, 0.816040, 0.841368, 0.858992, 0.873773, 0.885881, 0.900523, 0.915344, 0.929774, 0.939798, 0.950042, 0.962399, 0.968204, 0.970958, 0.975734, 0.981824, 0.986343, 0.992673, 0.993414, 0.995410, 0.997931, 1.000000, 0.999860, 0.997476, 0.992981, 0.991523, 0.995583, 0.994843, 0.992621, 0.988573, 0.981661, 0.976992, 0.970282, 0.957811, 0.945250, 0.935463, 0.924735, 0.911861, 0.894891, 0.875673, 0.853912, 0.829581, 0.800928, 0.772311, 0.746186, 0.723912, 0.699601, 0.673284, 0.644950, 0.615699, 0.583216, 0.549339, 0.516426, 0.483577, 0.449650, 0.417677, 0.384197, 0.342482, 0.299194, 0.251046, 0.203717, 0.143021, 0.065645.
- 95. A computer readable storage medium storing computer readable data comprising an optimized window for use with a linear predictive analysis procedure of an ITU-T G 729 standard, the optimized window comprising a first plurality of sample values, wherein the first plurality of sample values comprises: 0.006415, 0.014344, 0.020862, 0.026466, 0.032741, 0.038221, 0.043563, 0.049250, 0.055802, 0.061948, 0.068462, 0.075503, 0.082891, 0.091060, 0.099387, 0.107183, 0.115549, 0.125696, 0.136339, 0.145789, 0.153726, 0.164265, 0.177223, 0.190620, 0.203830, 0.218639, 0.233720, 0.249049, 0.265556, 0.283663, 0.301964, 0.321712, 0.342502, 0.366081, 0.387070, 0.409486, 0.433703, 0.459761, 0.484018, 0.506433, 0.529354, 0.554275, 0.573650, 0.588944, 0.604544, 0.625227, 0.643944, 0.657806, 0.671353, 0.685982, 0.698897, 0.711467, 0.725355, 0.741354, 0.756273, 0.765480, 0.775370, 0.784991, 0.794184, 0.803647, 0.813314, 0.820924, 0.828048, 0.837550, 0.847912, 0.859458, 0.864498, 0.872769, 0.881746, 0.887154, 0.893044, 0.903660, 0.911780, 0.921050, 0.929696, 0.938064, 0.948338, 0.962459, 0.971763, 0.981208, 0.985637, 0.988682, 0.989031, 0.992217, 0.994877, 0.997749, 1.000000, 0.997620, 0.992235, 0.989169, 0.983648, 0.977653, 0.971034, 0.965202, 0.956660, 0.947502, 0.935108, 0.925332, 0.914033, 0.898499, 0.878527, 0.863358, 0.849252, 0.832491, 0.810874, 0.788575, 0.762177, 0.731820, 0.699031, 0.663705, 0.627703, 0.592690, 0.556744, 0.514179, 0.461483, 0.407341, 0.345522, 0.281674, 0.196834, 0.091395.
- 96. A computer readable storage medium storing computer readable data comprising an optimized window for use with a linear predictive analysis procedure of an ITU-T G 729 standard, the optimized window comprising a first plurality of sample values, wherein the first plurality of sample values comprises: 0.018978, 0.041846, 0.060817, 0.076819, 0.093595, 0.108198, 0.122666, 0.138033, 0.154986, 0.171591, 0.189209, 0.207549, 0.226215, 0.245981, 0.266572, 0.284281, 0.304491, 0.328674, 0.351175, 0.367542, 0.380520, 0.399448, 0.420786, 0.437700, 0.453915, 0.472322, 0.489550, 0.503780, 0.518673, 0.530716, 0.543991, 0.558394, 0.574137, 0.587292, 0.598577, 0.610690, 0.622885, 0.634574, 0.644980, 0.655282, 0.669466, 0.686476, 0.700466, 0.709844, 0.719805, 0.733387, 0.745502, 0.754031, 0.764355, 0.778127, 0.789710, 0.799068, 0.812027, 0.827640, 0.844369, 0.857770, 0.869695, 0.886236, 0.906606, 0.924391, 0.934815, 0.943317, 0.948257, 0.955726, 0.965829, 0.975723, 0.980533, 0.985198, 0.992322, 0.994076, 0.992745, 0.993815, 0.994970, 0.996295, 1.000000, 0.997513, 0.996372, 0.997335, 0.994443, 0.990290, 0.985497, 0.978662, 0.972400, 0.972717, 0.969570, 0.964077, 0.957477, 0.949231, 0.940475, 0.930178, 0.915011, 0.899944, 0.887190, 0.874297, 0.859036, 0.838769, 0.817087, 0.792972, 0.765056, 0.733384, 0.701939, 0.673224, 0.649277, 0.625261, 0.598574, 0.570586, 0.541216, 0.510761, 0.478517, 0.447402, 0.416432, 0.385819, 0.356005, 0.325158, 0.288197, 0.252122, 0.212228, 0.171692, 0.119241, 0.053863.
- 97. A computer readable storage medium storing computer readable data comprising an optimized window for use with a linear predictive analysis procedure s of an ITU-T G 729 standard, the optimized window comprising a first plurality of sample values, wherein the first plurality of sample values comprises: 0.032368, 0.070992, 0.104001, 0.130989, 0.158618, 0.183311, 0.209813, 0.235893, 0.263139, 0.290663, 0.319418, 0.349405, 0.380787, 0.413518, 0.446571, 0.475812, 0.508718, 0.548017, 0.584584, 0.607285, 0.623716, 0.648710, 0.673015, 0.691285, 0.710126, 0.730009, 0.748768, 0.763481, 0.778534, 0.790593, 0.803461, 0.814148, 0.826917, 0.836676, 0.844328, 0.853257, 0.862934, 0.870774, 0.876733, 0.883246, 0.892043, 0.903228, 0.911752, 0.916944, 0.922037, 0.928852, 0.934055, 0.937002, 0.941260, 0.947170, 0.949587, 0.950625, 0.955168, 0.960953, 0.968763, 0.972807, 0.973065, 0.976498, 0.982413, 0.986591, 0.988961, 0.989838, 0.989248, 0.992486, 0.995513, 0.998614, 0.999549, 1.000000, 0.999652, 0.997571, 0.992708, 0.988906, 0.987096, 0.985167, 0.986103, 0.982236, 0.978635, 0.977097, 0.973180, 0.967504, 0.960993, 0.951541, 0.942105, 0.941105, 0.939154, 0.932846, 0.923188, 0.912594, 0.903162, 0.891309, 0.874549, 0.857906, 0.843536, 0.829542, 0.813114, 0.791248, 0.766908, 0.736502, 0.699416, 0.659532, 0.621899, 0.586649, 0.559063, 0.531663, 0.502472, 0.473266, 0.443670, 0.413039, 0.382995, 0.354757, 0.327742, 0.301987, 0.275724, 0.248407, 0.217190, 0.187928, 0.157322, 0.127304, 0.087168, 0.038800.
- 98. A computer readable storage medium storing computer readable data comprising an optimized window for use with a linear predictive analysis procedure of an ITU-T G 729 standard, the optimized window comprising a first plurality of sample values, wherein the first plurality of sample values comprises: 0.022638, 0.049893, 0.073398, 0.091759, 0.110170, 0.126403, 0.143979, 0.161140, 0.178336, 0.194547, 0.211645, 0.231052, 0.251342, 0.271996, 0.292451, 0.312423, 0.333549, 0.355545, 0.376768, 0.396785, 0.417081, 0.442956, 0.473160, 0.502298, 0.530133, 0.558464, 0.590280, 0.624473, 0.662582, 0.692886, 0.712825, 0.733828, 0.751837, 0.770836, 0.787658, 0.805155, 0.820733, 0.834659, 0.845647, 0.855709, 0.866900, 0.882317, 0.895480, 0.905044, 0.913294, 0.923179, 0.930585, 0.937805, 0.945655, 0.953583, 0.958026, 0.961559, 0.964647, 0.971273, 0.980345, 0.983826, 0.984393, 0.986661, 0.988407, 0.990593, 0.992878, 0.992387, 0.993311, 0.995638, 0.996021, 0.997546, 1.000000, 0.999479, 0.998087, 0.995468, 0.992561, 0.991342, 0.989436, 0.987899, 0.988164, 0.985124, 0.982922, 0.983393, 0.977788, 0.974029, 0.969894, 0.964447, 0.958461, 0.957896, 0.955135, 0.951701, 0.946896, 0.939734, 0.933706, 0.928074, 0.919777, 0.909893, 0.900927, 0.892969, 0.883315, 0.871214, 0.859219, 0.848186, 0.834842, 0.817133, 0.796229, 0.778367, 0.762923, 0.743623, 0.719600, 0.694968, 0.664921, 0.625471, 0.578317, 0.527732, 0.480384, 0.438591, 0.402137, 0.362915, 0.316804, 0.271267, 0.224062, 0.178894, 0.121786, 0.054482.
- 99. A computer readable storage medium storing computer readable data comprising an optimized window for use with a linear predictive analysis procedure of an ITU-T G 729 standard, the optimized window comprising a first plurality of sample values, wherein the first plurality of sample values comprises: 0.020460, 0.045083, 0.066383, 0.083309, 0.100691, 0.116443, 0.132084, 0.146273, 0.160321, 0.174568, 0.189298, 0.203568, 0.217862, 0.232409, 0.247273, 0.260606, 0.273681, 0.286389, 0.300298, 0.312947, 0.324128, 0.338319, 0.356184, 0.372224, 0.388061, 0.404936, 0.422500, 0.438661, 0.458192, 0.478784, 0.500707, 0.525751, 0.552009, 0.579318, 0.604901, 0.632992, 0.663769, 0.697784, 0.729886, 0.755063, 0.775634, 0.801067, 0.820260, 0.835611, 0.847438, 0.863815, 0.880576, 0.893437, 0.904934, 0.917732, 0.927039, 0.936925, 0.945466, 0.955971, 0.966724, 0.972415, 0.977788, 0.983337, 0.987107, 0.989729, 0.993216, 0.993077, 0.993032, 0.993864, 0.994757, 0.995481, 0.998028, 1.000000, 0.999625, 0.994891, 0.991095, 0.989700, 0.987494, 0.983622, 0.979496, 0.974914, 0.970786, 0.968301, 0.961302, 0.953409, 0.946868, 0.939263, 0.930691, 0.927281, 0.923373, 0.917657, 0.912348, 0.902403, 0.892379, 0.883578, 0.875732, 0.864583, 0.854513, 0.846606, 0.837772, 0.826760, 0.816543, 0.807560, 0.796882, 0.779644, 0.760555, 0.745676, 0.733771, 0.718454, 0.699926, 0.679620, 0.656820, 0.631938, 0.604826, 0.574119, 0.543804, 0.516049, 0.488212, 0.453966, 0.408583, 0.364608, 0.314635, 0.258365, 0.179497, 0.084086.
- 100. A computer readable storage medium storing computer readable program code for determining a plurality of optimized unquantized LP coefficients and optimized quantized LP coefficients in a G.729 speech coding standard, the computer readable program code comprising:
data encoding an optimized G.729 window; and a computer code implementing an improved linear prediction analysis procedure in response to a speech signal, wherein the improved linear prediction analysis procedure:
(A) high pass filters and scales the speech signal to create a preprocessed speech signal, wherein the preprocessed speech signal comprises a plurality of segments; (B) windows the preprocessed speech signal with an optimized G.729 window to create a frame of the preprocessed speech signal, wherein the frame comprises one of the plurality of segments of the preprocessed speech signal and includes a first subframe and a second subframe; (C) determines the plurality of optimized unquantized LP coefficients for the frame through autocorrelation; (D) transforms the plurality of optimized unquantized LP coefficients for the frame into optimized LSP coefficients for the second subframe of the frame; (E) quantizes the plurality of optimized LSP coefficients for the second frame of the frame to create quantized optimized LSP coefficients for the second frame of the frame; (F) interpolates the quantized optimized LSP coefficients for the second frame of the frame with optimized LSP coefficients for a second frame of a prior frame to create quantized optimized LP coefficients of the first subframe of the frame; (G) transforms the quantized optimized LSP coefficients of the first and second subframes of the frame into quantized optimized LP coefficients of the first and second subframes, respectively; and (H) repeats steps (B) through (G) for each of the plurality of segments of the preprocessed speech signal.
- 101. The computer readable storage medium, as claimed in claim 100, further comprising data encoding an optimized LSP interpolation factor and the improved linear prediction analysis procedure interpolates the quantized optimized LSP coefficients for the second frame of the frame with optimized LSP coefficients for a second frame of a prior frame using the optimized LSP interpolation factor to create the quantized optimized LP coefficients of the first subframe of the frame.
- 102. A computer readable storage medium storing computer readable program code for determining a plurality of optimized unquantized LP coefficients and optimized quantized LP coefficients in a G.729 speech coding standard, the computer readable program code comprising:
data encoding an optimized LSP interpolation factor; and a computer code implementing an improved linear prediction analysis procedure in response to a speech signal, wherein the improved linear prediction analysis procedure:
(A) high pass filters and scales the speech signal to create a preprocessed speech signal, wherein the preprocessed speech signal comprises a plurality of segments; (B) windows the preprocessed speech signal with a G.729 window to create a frame of the preprocessed speech signal, wherein the frame comprises one of the plurality of segments of the preprocessed speech signal and includes a first subframe and a second subframe; (C) determines the plurality of unquantized LP coefficients for the frame through autocorrelation; (D) transforms the plurality of unquantized LP coefficients for the frame into LSP coefficients for the second subframe of the frame; (E) quantizes the plurality of LSP coefficients for the second frame of the frame to create quantized LSP coefficients for the second frame of the frame; (F) interpolates the quantized LSP coefficients for the second frame of the frame with LSP coefficients for a second frame of a prior frame using the optimized LSP interpolation factor to create quantized optimized LP coefficients of the first subframe of the frame; (G) transforms the quantized optimized LSP coefficients of the first subframe of the frame and the quantized LSP coefficients of the second subframe of the frame into quantized optimized LP coefficients of the first subframe of the frame and quantized LP coefficients of the second subframe of the frame, respectively, and (H) repeats steps (B) through (G) for each of the plurality of segments of the preprocessed speech signal.
- 103. A computer readable storage medium storing computer readable program code for determining a synthesis filter and an excitation signal for each frame of a speech signal, the computer readable program code comprising:
data encoding an optimized G.729 window; and a computer code implementing an improved G.729 standard in response to a speech signal, wherein the improved G.729 standard performs an improved linear predictive analysis procedure using the optimized G.729 window to produce optimized unquantized LP coefficients for the frame and optimized quantized LP coefficients for a first and second subframe of the frame; defines the synthesis filter for the frame using the optimized unquantized LP coefficients and determines the excitation signal for the first and second subframes using the optimized quantized LP coefficients; and repeats performing the improved linear predictive analysis procedure and defining the synthesis filter for each frame of the speech signal and determining the excitation signal for the first and second subframes for each frame of the speech signal.
- 104. The computer readable storage medium, as claimed in claim 103, further comprising, data encoding an optimized LSP interpolation factor, and wherein improved linear prediction analysis procedure interpolates the quantized optimized LSP coefficients for the second frame of the frame with optimized LSP coefficients for a second frame of a prior frame using the optimized LSP interpolation factor to create the quantized optimized LP coefficients of the first subframe of the frame.
- 105. A computer readable storage medium storing computer readable program code for determining a synthesis filter and an excitation signal for each frame of a speech signal, the computer readable program code comprising:
data encoding an optimized LSP interpolation factor; and a computer code implementing an improved G.729 standard in response to a speech signal, wherein the improved G.729 standard performs an improved linear predictive analysis procedure using the optimized LSP interpolation factor to produce unquantized LP coefficients for the frame, quantized LP coefficients for a s first subframe of the frame and optimized quantized LP coefficients for a second subframe of the frame; defines the synthesis filter for the frame using the unquantized LP coefficients and determines an excitation signal for the first and second subframes using the quantized LP coefficients and the optimized quantized LP coefficients; and repeats performing the improved linear predictive analysis procedure and defining the synthesis filter for each frame of the speech signal and determining an excitation signal for the first and second subframes for each frame of the speech signal.
- 106. An optimization device for optimizing a G.729 window, comprising:
a memory device, wherein the memory device stores an alternate window optimization procedure and the G.729 window; an interface; a processor, coupled to the interface and the memory device, wherein the processor receives training data from the interface via an interface signal and optimizes the G.729 window using the training data and the alternate window optimization procedure to produce an optimized G.729 window, wherein the G.729 window and the alternate window optimization procedure are communicated to the processor by the memory device via a memory signal, and the processor communicates the optimized G.729 window to the memory device via processor signal.
- 107. An optimization device for optimizing a G.729 LSP interpolation factor, comprising:
a memory device, wherein the memory device stores an LSP interpolation factor optimization procedure and the G.729 LSP interpolation factor; an interface; a processor, coupled to the interface and the memory device, wherein the processor receives training data from the interface via an interface signal and optimizes the G.729 LSP interpolation factor using the training data and the LSP interpolation factor optimization procedure to produce an optimized G.729 LSP interpolation factor, wherein the G.729 LSP interpolation factor and the LSP interpolation factor optimization procedure are communicated to the processor by the memory device via a memory signal, and the processor communicates the optimized G.729 LSP interpolation factor to the memory device via processor signal.
- 108. An optimization device for optimizing a G.729 window and a G.729 LSP interpolation factor, comprising:
a memory device, wherein the memory device stores a joint window and LSP interpolation factor optimization procedure, the G.729 window and the G.729 LSP interpolation factor; an interface; a processor, coupled to the interface and the memory device, wherein the processor receives training data from the interface via an interface signal and optimizes the G.729 window and the G.729 LSP interpolation factor using the training data and the joint window and LSP interpolation factor optimization procedure to produce an optimized G.729 window and an optimized LSP interpolation factor, wherein the G.729 window, the G.729 LSP interpolation factor and the joint window and LSP interpolation factor optimization procedure are communicated to the processor by the memory device via a memory signal, and the processor communicates the optimized G.729 window and the optimized G.729 LSP interpolation factor to the memory device via processor signal.
RELATED APPLICATIONS
[0001] The application is a continuation-in-part of the US patent application entitled “Method and Apparatus for Gradient-Descent Based Window Optimization for Linear Prediction Analysis,” application serial number not yet assigned, Attorney Docket Number 10745-162, filed Nov. 2, 2002, which is incorporated herein by reference; and the US patent application entitled “Optimized Window and Methods Therefore for Gradient-Descent Based Window Optimization for Linear Prediction Analysis in the ITU-T G.723.1 Speech Coding Standard,” application serial number not yet assigned, Attorney Docket Number 10745-163, filed Dec. 17, 2002, which is incorporated herein by reference.
Continuation in Parts (1)
|
Number |
Date |
Country |
Parent |
10282966 |
Oct 2002 |
US |
Child |
10366821 |
Feb 2003 |
US |