Method of use of voice message coder/decoder

• US 5,327,520 A
• Filed: 06/04/1992
• Issued: 07/05/1994
• Est. Priority Date: 06/04/1992
• Status: Expired due to Term

• Current Assignee: American Telephone & Telegraph Company (AT&T, Inc.)
• Original Assignee: AT&T, Inc.
• Inventors: Chen, Juin-Hwey
• Primary Examiner(s): Fleming, Michael R.
• Assistant Examiner(s): Doerrler, Michelle
• Application Number:
  US07/893,296
• Time in Patent Office: 761 Days
• Field of Search: 381/29-53, 395/2, 395/2.28, 395/2.31
• US Class Current: 704/219
• CPC Class Codes: G10L 19/06 Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
• CPC Class Codes: G10L 19/12 the excitation function being a code excitation, e.g. in code excited linear prediction [CELP] vocoders
• CPC Class Codes: G10L 2019/0003 Backward prediction of gain
• CPC Class Codes: G10L 2019/0011 Long term prediction filters, i.e. pitch estimation
• CPC Class Codes: G10L 2019/0013 Codebook search algorithms
• CPC Class Codes: G10L 25/06 the extracted parameters being correlation coefficients

Abstract

A code excited linear predictive coder and decoder well suited to speech recording, transmission and reproduction, especially in voice messaging systems, provides backward adaptive gain control of stored codevectors to be applied to a synthesis filter prior to being compared with sequences of input speech signals. Simplified linear predictive parameter quantization using efficient table lookup procedures, efficient codevector storage and search all contribute in an illustrative embodiment to high quality coding and decoding with reduced computational complexity.

Claims

1. A method of processing a sequence of input samples comprising

gain adjusting each of a plurality of codevectors in a backward adaptive gain controller to produce corresponding gain-adjusted codevectors, each of said codevectors being identified by a corresponding index,

filtering each of said gain-adjusted codevectors in a synthesis filter characterized by a plurality of filter parameters to generate candidate codevectors, the synthesis filter comprising a short term synthesis filter and a long term synthesis filter, the long term synthesis filter being forward adaptive,

comparing said sequence of input samples with each of said candidate codevectors to determine, for said sequence of input samples, a candidate codevector substantially approximating said sequence of input samples, and

outputting

(i) the index for the candidate codevector, and

(ii) the parameters of said long term synthesis filter.

2. The method of claim 1 wherein

said synthesis filter comprises a long-term filter component and a short-term filter component, each of said filter components being characterized by a respective plurality of filter parameters, and

wherein adjusting the parameters of said synthesis filter comprises adjusting the parameters of each of said filter components based on a linear predictive analysis of said sequence of input samples.

3. The method of claim 2 wherein said sequence of input samples is a current sequence of input samples in a plurality of consecutive sequences of input samples, said plurality of sequences of input samples including at least one sequence of input samples preceding the current sequence of input samples, and

said linear predictive analysis of said input samples comprises

grouping the plurality of consecutive sequences of input samples into a frame of input samples, each of said sequences of input samples thereby comprising a sub-frame,

determining a set of Nth order predictor coefficients corresponding to said frame of input samples wherein N is the number of predictor coefficients.

4. The method of claim 3, wherein said determining said set of Nth order predictor coefficients, comprises

performing an autocorrelation analysis of said frame of input samples to generate a set of autocorrelation coefficients, and

recursively forming said predictor coefficients based on said autocorrelation coefficients.

5. The method of claim 3, further comprising

weighting said frame of input samples to form a weighted frame of input samples prior to determining said Nth order predictor coefficients, and

wherein said determining said set of Nth order predictor coefficients, comprises

performing an autocorrelation analysis of said weighted frame of input samples to generate an ordered set of autocorrelation coefficients, and

performing a Levinson-Durbin recursion based on said autocorrelation coefficients to determine said set of predictor coefficients.

6. The method of claim 5, further comprising

modifying said autocorrelation coefficients to reflect the addition of a small amount of white noise.

7. The method of claim 6, wherein said modifying comprises changing the first of said autocorrelation coefficients by a small factor.

8. The method of claim 7, further comprising the step of modifying the bandwidth of the set of predictor coefficients, thereby expanding the spectral peaks of said synthesis filter.

9. The method of claim 3, further comprising recursively converting said set of predictor coefficients into a set of reflection coefficients according to ##EQU25## where, k_m is the m-th reflection coefficient and a_i.sup.(m) is the i-th coefficient of the m-th order predictor.

10. The method of claim 9 wherein each of said frames comprises S sub-frames and

said method further comprises

weighting said frame of input samples, thereby forming weighted input samples, prior to determining said Nth order predictor coefficients, and

determining predictor coefficients for each weighted sub-frame of input samples based on an interpolation of predictor coefficients determined for a current frame and the predictor coefficients for the immediately preceding frame.

11. The method of claim 10 wherein

S=4, so that each of said frames comprises four sub-frames of input samples,

said weighting is in accordance with a shaped weighting window function centered on the fourth of said sequences of input samples, and

said interpolation is performed in accordance with ##EQU26## where k_m and k_m are the m-th quantized reflection coefficients of the previous frame and the current frame, respectively, and k_m (j) is the interpolated m-th reflection coefficient for the j-th weighted sequence of input samples.

12. The method of claim 9, comprising the further step of quantizing said set of reflection coefficients by

comparing each of said reflection coefficients with indexed elements of threshold values identifying quantizer cell boundaries, thereby to determine an index identifying a quantizer cell, and

based on the index identified for each reflection coefficient, assigning a quantizer output value corresponding to a quantizer cell.

13. The method of claim 12, wherein each of said threshold values is an inverse transform value of a quantizer cell boundary value from a transform domain range of values.

14. The method of claim 12, wherein

said indexed elements of threshold values are stored in an ordered table of threshold values, with each threshold value having a uniquely associated index, and

said comparing to determine an index value comprises searching of values in said table to find a value meeting a predetermined criterion.

15. The method of claim 14, wherein said searching comprises a binary tree search of said table based on the value of said reflection coefficients.

16. The method of claim 2, wherein said adjusting of the parameters of said long-term filter further comprises

extracting a pitch lag parameter based on said linear predictive analysis of each of said sequences of input samples, and wherein

said outputting parameters of said synthesis filter comprises outputting a coded representation of said pitch lag parameter for each sequence of input samples.

17. The method of claim 2, wherein said adjusting of the parameter of said long-term filter further comprises

grouping a plurality of consecutive sequences of input samples into a frame of input samples, each of said sequences of input samples thereby comprising a sub-frame

extracting a pitch lag parameter for each subframe based on said linear predictive analyses of said subframe, and wherein

said outputting parameters of said synthesis filter comprises outputting a coded representation of said pitch lag parameter and said pitch predictor tap weights for each subframe.

18. The method of claim 17, wherein said extracting of a pitch lag parameter comprises

generating a set of signals representing LPC residuals for the current subframe of input samples,

forming a cross correlation, for each of a range of lag values, based on said LPC residuals for the current frame and the LPC residuals for a plurality of prior subframes,

selecting a pitch lag parameter based on the lag value of said cross correlation having the largest value.

19. The method of claim 18, wherein

said LPC residuals for said current subframe and for said prior subframes are time decimated prior to said cross correlation, and

said method further comprises adjusting said selected value of said lag parameter to reflect the time decimation.

20. The method of claim 17, wherein

said plurality of tap weights comprises three tap weights,

said long-term filter component has a transfer function given by ##EQU27## said storing one or more pitch tap vectors corresponding to each possible set of quantized tap weights comprises storing a vector given by

21. The method of claim 1 wherein said sequence of input samples is a current sequence of input samples in a plurality of consecutive sequences of input samples, said plurality of consecutive sequences of input samples having at least one sequence of input samples preceding said current sequence of input samples, said synthesis filter comprising memory, said memory storing a residual signal reflecting codevector information corresponding to said at least part of at least one sequence of input samples preceding said current sequence of input samples, said residual signal giving rise to a contribution to said candidate codevectors, the method further comprising

removing said contribution to said candidate codevectors prior to said comparing.

22. The method of claim 1, wherein said comparing comprises

perceptually weighting said input samples and said candidate codevectors prior to said comparing.

23. The method of claim 22 wherein said sequence of input samples is a current sequence of input samples in a plurality of consecutive sequences of input samples, said plurality of consecutive sequences of input samples havinq at least one sequence of input samples preceding said current sequence of input samples, said synthesis filter comprising memory, said memory storinq a residual signal reflecting codevector information corresponding to said at least part of at least one sequence of input samples preceding said current sequence of input samples, said residual signal giving rise to a contribution to said candidate codevectors, the method further comprising

removing said contribution to said candidate codevectors prior to said comparing.

24. The method of claim 1 wherein

said plurality of codevectors comprises M/2 linearly independent codevectors, where M is the number of codevectors that are gain adjusted,

said comparing comprises comparing M codevectors, said M codevectors being based on said M/2 linearly independent codevectors and each of two sign values for said codevectors.

25. The method of claim 1, wherein said backward adaptive gain controller is adaptively adjusted by the further step of

passing gain information relating to said codevector corresponding to said outputted index through said gain controller.

26. The method of claim 1 further comprising storing said outputted index and parameters.

27. The method of claim 1 further comprising transmitting said outputted index and parameters to a communications medium.

28. The method of claim 1 for processing a set of additional sequences of input samples, the set of additional sequences of input samples being subsequent to the sequence of input samples previously processed, the method comprising:

(a) adjusting the parameters of the synthesis filter in response to a previous sequence of input samples;

(b) repeating the steps of gain adjusting, filtering, comparing, and outputting for a next sequence of input samples from the set of additional sequences of input samples; and

(c) repeating steps (a) and (b) until each sequence in the set of additional sequences of input samples has been processed.

29. The method of claim 1 wherein the step of comparing further comprises determining the candidate codevector having the minimum difference relative to the sequence of input samples.

30. A method of processing a sequence of input samples comprising:

(a) gain adjusting the sequence of input samples in a backward adaptive gain controller to produce a gain-adjusted sequence of input samples;

(b) filtering each of a plurality of codevectors in a synthesis filter characterized by a plurality of filter parameters to generate a plurality of candidate codevectors, the synthesis filter comprising a short term synthesis filter and a long term synthesis filter, the long term synthesis filter being forward adaptive, each of the plurality of codevectors having an index associated therewith;

(c) comparing the plurality of candidate codevectors with the gain-adjusted sequence of input samples to determine a candidate codevector substantially approximating the gain-adjusted sequence of input samples; and

(d) outputting

(i) the index associated with the candidate codevector substantially approximating the gain-adjusted sequence of input samples; and

(ii) the parameters of said long term synthesis filter.

31. The method of claim 30 for processing a set of additional sequences of input samples, the set of additional sequences of input samples being subsequent to the sequence of input samples previously processed, the method comprising:

(a) adjusting the parameters of the synthesis filter in response to a previous sequence of input samples;

(b) repeating steps (a) through (d) for a next sequence of input samples from the set of additional sequences of input samples; and

(c) repeating steps (a) and (b) until each additional sequence in the set of additional sequences of input samples has been processed.

32. The method of claim 31 wherein adjusting the parameters of the synthesis filter comprises adjusting parameters of the long term filter comprising:

(a) grouping a plurality of consecutive sequences of input samples into a frame of input samples, each of the sequences of input samples thereby comprising a sub-frame; and

(b) extracting a pitch lag parameter for each sub-frame based on the linear predictive analysis of the sub-frame.

33. The method of claim 32 wherein outputting the parameters of the synthesis filter comprises outputting a coded representation of the pitch lag parameter for each sub-frame.

34. The method of claim 30 wherein adjusting the parameters of the synthesis filter is based upon a linear predictive analysis.

35. The method of claim 34 wherein the linear predictive analysis comprises:

(a) grouping a plurality of consecutive sequences of input samples into a frame of input samples;

(b) performing an autocorrelation analysis of the frame of input samples to generate a set of autocorrelation coefficients; and

(c) determining a set of Nth order predictor coefficients based on the set of autocorrelation coefficients.

36. The method of claim 30 wherein the step of comparing further comprises determining the candidate codevector having the minimum difference relative to the sequence of input samples.

38. The method of claim 32 wherein the step of producing a gain-adjusted first signal comprises leaving the first signal unchanged.

39. The method of claim 32 wherein the step of producing a gain-adjusted set of second signals comprises leaving the set of second signals unchanged.

40. The method of claim 32 for processing a set of additional first signals, the set of additional first signals being subsequent to the first signal previously processed, the method comprising:

(a) adjusting the parameters of the synthesis filter in response to a previous first signal;

(b) repeating steps (a) through (d) of claim 36 for a next first signal from the set of additional first signals; and

(c) repeating steps (a) and (b) until each additional first signal in the set of additional first signals has been processed.

41. The method of claim 40 wherein adjusting the parameters of the synthesis filter is based upon a linear predictive analysis.

42. The method of claim 41 wherein the linear predictive analysis comprises:

(a) grouping a plurality of consecutive first signals into a frame of input samples;

(b) performing an autocorrelation analysis of the frame of first signals to generate a set of autocorrelation coefficients; and

(c) determining a set of Nth order predictor coefficients based on the set of autocorrelation coefficients.

43. The method of claim 40 wherein adjusting the parameters of the synthesis filter comprises adjusting parameters of the long term filter comprising:

(a) grouping a plurality of consecutive first signals into a frame of input samples, each of the first signals thereby comprising a sub-frame; and

(b) extracting a pitch lag parameter for each sub-frame based on the linear predictive analysis of the sub-frame.

44. The method of claim 43 wherein outputting the parameters of the synthesis filter comprises outputting a coded representation of the pitch lag parameter for each sub-frame.

45. The method of claim 32 wherein the step of comparing further comprises determining a filtered second signal in the filtered set of second signals having the minimum difference relative to the first signal.

37. A method of processing a first signal by utilizing a set of second signals, the method comprising:

(a) in a backward adaptive gain controller, producing a gain-adjusted first signal and a gain-adjusted set of second signals;

(b) filtering the gain-adjusted set of second signals in a synthesis filter characterized by a plurality of filter parameters to generate a filtered set of second signals, the synthesis filter comprising a short term synthesis filter and a long term synthesis filter, the long term synthesis filter being forward adaptive, each signal in the filtered set of second signals having an index associated therewith;

(c) comparing each signal in the filtered set of second signals with the gain-adjusted first signal to determine a filtered second signal substantially approximating the gain-adjusted first signal; and

(d) outputting

(i) the index associated with the filtered second signal; and

(ii) the parameters of said long term synthesis filter.