# US 5,351,338 A · Time variable spectral analysis based on interpolation for speech coding > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 5,351,338 A - Document ID: US5351338A - Status: Granted - Kind: A - Application number: 07/909,012 - Filed: 1992-07-06 - Granted: 1994-09-27 - Earliest claimed: 1992-07-06 - Assignees: Telefonaktiebolaget L M Ericsson - Inventors: Torbjorn K. Wigren - Web page: https://patentgrep.com/patent/US5351338A ## CPC - G10L 19/06: Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract A time variable spectral analysis for speech coding based upon interpolation between speech frames. A speech signal is modeled by a linear filter which is obtained by a time variable linear predictive coding analysis algorithm. Interpolation between adjacent speech frames is used in order to express a time variation of the speech signal. In addition, interpolation between adjacent frames secures a continuous track of filter parameters across different speech frames. ## Claims ### Claim 1 A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames using time variable spectral models, the method comprising the steps of: sampling a signal to obtain a series of discrete samples and constructing therefrom a series of frames; modeling the spectrum of said signal using a filter model utilizing interpolation of parameter signals between a previous, present and next frame for forming estimated parameters; calculating regressor signals from said estimated parameters; smoothing the spectrum by combining the regressor signals with a smoothing parameter to obtain smoothed regressor signals; combining said smoothed regressor signals with weighting factors to produce a first set of signals; combining parameter signals from the previous frame with said smoothed regressor signals, a signal sample and a weighting factor to produce a second set of signals; calculating parameter signals for the present frame and the next frame from the first and second set of signals; determining whether the filter model is stable after each frame; and stabilizing the filter model if the filter model is determined to be unstable. ### Claim 2 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said filter model is a linear, time-varying all-pole filter. ### Claim 3 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said filter model includes a numerator. ### Claim 4 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said interpolation is piecewise constant. ### Claim 5 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said interpolation is piecewise linear. ### Claim 6 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said interpolation extends over more frames than said previous, present and next frames. ### Claim 7 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said interpolation is nonlinear. ### Claim 8 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein spectral smoothing is obtained by prewindowing of the estimated parameters. ### Claim 9 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein spectral smoothing is obtained by correlation weighting. ### Claim 10 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein a Schur-Cohn-Jury test is used to determine if said model is stable. ### Claim 11 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein the stability of said model is determined by calculating reflection coefficients and examining the reflection coefficients sizes. ### Claim 12 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein the stability of said model is determined by calculation of poles. ### Claim 13 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said model is stabilized by pole-mirroring. ### Claim 14 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said model is stabilized by bandwidth expansion. ### Claim 15 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said signal frame is a speech frame. ### Claim 16 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, said signal frame is a radar signal frame. ### Claim 17 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals for the present frame and the next frame are calculated using Gaussian elimination. ### Claim 18 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals for the present frame and the next frame are calculated using Gaussian elimination with LU-decomposition. ### Claim 19 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals for the present frame and the next frame are calculated using QR-factorization. ### Claim 20 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals for the present frame and the next frame are calculated using U-D-factorization. ### Claim 21 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals for the present frame and the next frame are calculated using Cholesky-factorization. ### Claim 22 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals for the present frame and the next frame are calculated using a Levenberg-Marquardt method. ### Claim 23 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals for the present frame and the next frame are calculated using a recursive formulation. ### Claim 24 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals are a-parameters. ### Claim 25 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals are reflection coefficients. ### Claim 26 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals are area coefficients. ### Claim 27 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals are log-area parameters. ### Claim 28 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals are log-area ratio parameters. ### Claim 29 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals are formant frequencies and corresponding bandwidths. ### Claim 30 (depends on 1) A method of linear predictive coding analysis and interpolating of uninterpolated input signal frames according to claim 1, wherein said parameter signals are arcsine parameters. ### Claim 31 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals are autocorrelation-parameters. ### Claim 32 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said parameter signals are line spectral frequencies. ### Claim 33 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein an additional known input signal to said spectral model is utilized. ### Claim 34 (depends on 1) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 1, wherein said filter model is non-linear in the parameter signals. ### Claim 35 A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames using time variable spectral models, the method comprising: sampling a signal to obtain a series of discrete samples and constructing therefrom a series of frames; modeling the spectrum of said signal using a filter model utilizing interpolation of parameters between a previous, present and next frame for forming estimated parameters; calculating regressor signals from said estimated parameters; smoothing the spectrum by combining the regressor signals with a smoothing parameter to obtain smoothed regressor signals; combining said smoothed regressor signals with a weighting factor to produce a first set of signals; combining parameter signals from the previous frame with said smoothed regressor signals, a signal sample and a weighting factor to produce a second set of signals; calculating parameter signals for the present frame from the first and second set of signals; determining whether the filter model is stable after each frame; stabilizing the filter model if the filter model is determined to be unstable. ### Claim 36 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said filter model is a linear, time-varying all-pole filter. ### Claim 37 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said filter model includes a numerator. ### Claim 38 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said interpolation is piecewise constant. ### Claim 39 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said interpolation is piecewise linear. ### Claim 40 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said interpolation extends over more frames than said previous, present and next frames. ### Claim 41 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said interpolation is nonlinear. ### Claim 42 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein spectral smoothing is obtained by prewindowing of the estimated parameters. ### Claim 43 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein spectral smoothing is obtained by correlation weighting. ### Claim 44 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein a Schur-Cohn-Jury test is used to determine if said model is stable. ### Claim 45 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein the stability of said model is determined by calculating reflection coefficients and examining the reflection coefficients sizes. ### Claim 46 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein the stability of said model is determined by calculation of poles. ### Claim 47 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said model is stabilized by pole-mirroring. ### Claim 48 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said model is stabilized by bandwidth expansion. ### Claim 49 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said signal frame is a speech frame. ### Claim 50 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said signal frame is a radar signal frame. ### Claim 51 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter vector signal for the present frame is calculated using Gaussian elimination. ### Claim 52 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal for the present frame is calculated using Gaussian elimination with LU-decomposition. ### Claim 53 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal for the present frame is calculated using QR-factorization. ### Claim 54 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal for the present frame is calculated using U-D-factorization. ### Claim 55 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal for the present frame is calculated using Cholesky-factorization. ### Claim 56 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal for the present frame is calculated using a Levenberg-Marquardt method. ### Claim 57 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal for the present frame is calculated using a recursive formulation. ### Claim 58 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is an a-parameter. ### Claim 59 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is a reflection coefficient. ### Claim 60 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is an area coefficient. ### Claim 61 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is a log-area parameter. ### Claim 62 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is a log-area ratio parameter. ### Claim 63 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is a formant frequency and a corresponding bandwidth. ### Claim 64 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is an arcsine parameter. ### Claim 65 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is an autocorrelation-parameter. ### Claim 66 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said parameter signal is a line spectral frequency. ### Claim 67 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein an additional known input signal to said spectral filter model is utilized. ### Claim 68 (depends on 35) A method of linear predictive coding analysis and interpolation of uninterpolated input signal frames according to claim 35, wherein said filter model is non-linear in the parameter signals. ### Claim 69 A method of signal coding, the method comprising: determining a first spectral analysis of signal frames using time variable spectral models and utilizing interpolation of spectral parameters between frames; determining a second spectral analysis using time invariant spectral models; comparing the first analysis to the second spectral analyses to determine which spectral analysis has the highest quality; and selecting the spectral analysis with the highest quality to code the signal. ### Claim 70 (depends on 69) A method of signal coding according to claim 69, wherein said spectral analyses are compared by measuring the signal energy reduction after synthesis filtering with said time variable and time invariant spectral models, and choosing the spectral analysis that gives the highest signal energy reduction. ### Claim 71 (depends on 70) A method of signal coding according to claim 70, further comprising the step of: determining is said first spectral analysis gives a stable model, wherein said spectral analysis is selected as said first spectral analysis if said first spectral analysis gives a stable model, and said second spectral analysis is selected if said first spectral analysis given an unstable model.