Speech coder and method having spectral interpolation and fast codebook search

• US 5,195,168 A
• Filed: 03/15/1991
• Issued: 03/16/1993
• Est. Priority Date: 03/15/1991
• Status: Expired due to Term

• Current Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Codex Corporation (Motorola Solutions, Inc.)
• Inventors: Yong, Mei
• Primary Examiner(s): Fleming, Michael R.
• Assistant Examiner(s): Doerrler, Michelle
• Application Number:
  US07/669,831
• Time in Patent Office: 732 Days
• Field of Search: 381/29-41, 395/2, 358/133
• US Class Current: 704/220
• CPC Class Codes: G10L 19/083 the excitation function being an excitation gain G10L25/90 takes precedence
• 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 2019/0012 Smoothing of parameters of the decoder interpolation
• CPC Class Codes: G10L 2019/0014 Selection criteria for distances

Abstract

A novel spectral interpolation and efficient excitation codebook search method developed for a Code-Excited Linear Predictive (CELP) speech coder is set forth. The interpolation is performed on an impulse response of the spectral synthesis filter. As the result of using this new set of interpolation parameters, the computations associated with an excitation codebook search in a CELP coder are considerably reduced. Furthermore, a coder utilizing this new interpolation approach provides noticeable improvement in speech quality coded at low bit-rates.

Claims

1. A method for reconstructing a signal that has been partitioned into successive time interval partitions, each time interval signal partition having a representative input reference signal with a set of vectors, and having at least a first representative electrical signal for each representative input reference signal of each time interval signal partition, the method utilizing at least a codebook unit having at least a codebook memory, a synthesis unit having at least a first synthesis filter, a combiner, and a perceptual weighting unit having at least a first perceptual weighting filter, for utilizing the electrical signals of the representative input reference signals to at least generate a related set of synthesized signal vectors for reconstructing the signal, the method comprising the steps of:

(1A) utilizing the at least first representative electrical signal for each representative input reference signal for a time signal partition to obtain a set of uninterpolated parameters for the at least first synthesis filter;

(1B) utilizing the at least first synthesis filter to obtain the corresponding impulse response representation, and interpolating the impulse responses of each adjacent time signal partition and of a current time signal partition immediately thereafter to provide a set of interpolated synthesis filters for desired subpartitions; and

utilizing the interpolated synthesis filters to provide a corresponding set of interpolated perceptual weighting filters for desired subpartitions;

such that smooth transitions of the synthesis filter and the perceptual weighting filter between each pair of adjacent partitions are obtained;

(1C) utilizing the set of input reference signal vectors, the related set of interpolated synthesis filters and the related set of interpolated perceptual weighting filters for the current time signal partition to select the corresponding set of optimal excitation codevectors from the at least first codebook memory, further implementing the following steps for each desired input reference signal vector;

(1C1) providing a particular excitation codevector which is associated with a particular index from the at least first codebook memory, the codebook memory having a set of excitation codevectors stored therein responsive to the representative input vectors;

(1C2) inputting the particular excitation codevector into the corresponding interpolated synthesis filter to produce the synthesized signal vector;

(1C3) subtracting the synthesized signal vector from the input reference signal vector related thereto to obtain a corresponding reconstruction error vector;

(1C4) inputting the reconstruction error vector into the corresponding interpolated perceptual weighting unit to determine a corresponding perceptually weighted squared error;

(1C5) determining and storing index of codevector having the perceptually weighted squared error smaller than all other errors produced by other codevectors;

(1C6) repeating the steps (1C1), (1C2), (1C3), (1C4), and (1C5) for every excitation codevector in the codebook memory and implementing these steps utilizing a fast codebook search method, to determine an optimal excitation codevector for producing the minimum weighted squared error among all excitation codevectors for the related input reference signal vector; and

(D) successively inputting the set of optimal excitation codevectors into the corresponding set of interpolated synthesis filters to produce the related set of synthesized signal vectors for the given input reference signal for reconstructing the input signal.

2. The method of claim 1, wherein the signal is a speech waveform.

3. The method of claim 1, wherein the at least first synthesis filter is at least a first time-varying linear predictive coding synthesis filter (LPC-SF).

4. The method of claim 3, wherein the at least first LPC-SF has a transfer function substantially of a form:

##EQU22## where a_i '"'"'s, for i=1,2, . . . , p represent a set of estimated prediction coefficients obtained by analyzing the corresponding time signal partition and p represents a predictor order.

5. The method of claim 4, wherein LPC-SFs (linear predictive coding synthesis filters) of an adjacent time signal partition and of a time partition immediately thereafter are substantially of a form:

##EQU23## where a_i.sup.(j) '"'"'s, for i=1, 2, 3, . . . , p and j=1, 2 represent a set of prediction coefficients in an adjacent time signal partition when j=1 and of a current time signal partition immediately thereafter when j=2, respectively, p represents a predictor order such that

an impulse response for the transfer function H.sup.(j) (z) is substantially of a form ##EQU24## where ∂

(n) is a unit sample function, and such that the impulse response of the at least first synthesis filter at an m-th subpartition of a current time partition obtained through linear interpolation of h.sup.(1) (n) and h.sup.(2) (n) respectively, denoted below as h_m (n), is substantially of a form;

space="preserve" listing-type="equation">h.sub.m (n)=α

.sub.m h.sup.(1) (n)+β

.sub.m h.sup.(2) (n),

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition, thereby providing a transfer function of the interpolated synthesis filter substantially of a form;

##EQU25## wherein the perceptual weighting filter at the m-th subpartition of a current time interval signal partition has a transfer function of a form;

##EQU26## where γ

is typically selected to be substantially 0.8.

6. The method of claim 5, wherein the interpolated synthesis filter is approximated by an all pole filter whose parameters are utilized in the LPC synthesis filter and in the perceptual weighting filter for interpolating subpartitions, wherein the all pole filter parameters are obtained utilizing the steps of:

truncating interpolated impulse samples;

estimating a first p+1 autocorrelation coefficients using the truncated interpolated impulse response samples; and

converting the autocorrelation coefficients to direct form prediction coefficients using a recursion algorithm.

7. The method of claim 6, wherein the estimated autocorrelation coefficients at the m-th subpartition can be expressed as:

##EQU27## for k=0,1, . . . , p and the summation is over all available partition impulse responses, such that ##EQU28## are autocorrelation coefficients of uninterpolated impulse response of the adjacent and current partitions, and ##EQU29## and i,j=1,2 where i≠

j, are cross-correlation coefficients between the uninterpolated impulse responses.

8. The method of claim 1, wherein the excitation code vectors are stored in memory.

9. The method of claim 1, wherein the perceptual weighting unit includes at least a first perceptual weighting filter having a transfer function substantially of a form:

##EQU30## where γ

is typically selected to be substantially 0.8.

10. The method of claim 1, wherein determining an optimal excitation codevector from the codebook memory for each input reference vector includes signal processing every excitation codevector in the codebook memory for each input reference vector, then determining the optimal excitation codevector of those codevectors processed.

11. The method of claim 1, wherein the fast codebook search method further includes utilizing a simplified method to obtain the perceptually weighted squared error between an input signal vector and a related synthesized codevector utilizing an i-th excitation codevector, denoting this error by E_i, such that:

##EQU31## where x represents an input target vector at a subpartition that is substantially equal to an input reference signal vector at a subpartition filtered by a corresponding interpolated weighting filter with a zero-input response of a corresponding interpolated weighted LPC-SF (linear predictive coding synthesis filter) subtracted from it, A_i represents a dot product of the vector x and an i-th filtered codevector y_i,m at an m-th subpartition, and B_i represents the squared norm of the vector y_i,m.

12. The method of claim 11, wherein the corresponding interpolated weighted LPC-SF has a transfer function of H_m (z/γ

), such that;

##EQU32## where for an m-th subpartition, γ

is typically selected to be 0.8, and a_i,m, for i=1,2, . . . p, such that p is a predictor order, represent the parameters of corresponding interpolated LPC-SF,

the impulse response of H_m (z/γ

), h_wm (n), is substantially equal to;

space="preserve" listing-type="equation">h.sub.wm (n)=γ

.sup.n h.sub.m (n),

and where h_m (n) is an impulse response of corresponding LPC-SF,

utilizing a fact that h_m (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated LPC-SFs, h_wm (n), at each interpolating subpartition, determined in a fast codebook search as a linear interpolation of two impulse responses of related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">h.sub.wm (n)=α

.sub.m h.sub.w.sup.(1) (n)+β

.sub.m h.sub.w.sup.(2) (n),

where h_w.sup.(j) (n)=γ

ⁿ h.sup.(j) (n) for j=1,2 are exponentially weighted uninterpolated impulse responses of the previous, when j=1, and the current, when j=2, LPC synthesis filters, and where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

13. The method of claim 12, wherein the filtered codevector y_i,m is determined as a convolution of the i-th excitation codevector c_i with the corresponding weighted impulse response h_wm (n), the convolution substantially of a form:

y_i,m =F_wm c_i, where ##EQU33## and where k represents a dimension of a codevector, further utilizing the fact that h_wm (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated weighted LPC-SFs, the filtered codevector y_i,m at each interpolating subpartition may be determined as linear interpolation of two codevectors filtered by the related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">y.sub.i,m =α

.sub.m y.sub.i.sup.(1) +β

.sub.m y.sub.i.sup.(2),

and where y_i.sup.(j) =F_w.sup.(j) c_i for j=1,2

and where matrices F_w.sup.(1) and F_w.sup.(2) have a same format as the matrix F_wm, but with different elements h_w.sup.(1) (n) and h_w.sup.(2) (n), respectively.

14. The method of claim 11, wherein the squared norm B_i at each interpolating subpartition is a weighted sum of a squared norm of a filtered codevector y_i.sup.(1), the squared norm of the filtered codevector y_i.sup.(2), and a dot product of those two filtered codevectors, substantially of a form:

space="preserve" listing-type="equation">B.sub.i =α

.sub.m.sup.2 ||y.sub.i.sup.(1) ||.sup.2 +β

.sub.m.sup.2 ||y.sub.i.sup.(2) ||.sup.2 +2α

.sub.m β

.sub.m <

y.sub.i.sup.(1) ·

y.sub.i.sup.(2) >

,

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

15. The method of claim 11, wherein determination of the dot product A_i for each interpolating subpartition comprises two steps:

16A) backward filtering such that z=F^t_wm x wherein ##EQU34## and where k represents a dimension of a codevector; and

where t represents a transpose operator; and

16B) forming a dot product such that;

space="preserve" listing-type="equation">A.sub.i =<

z·

c.sub.i >

,

where c_i is the ith excitation codevector.

16. The method of claim 1, further including, after step 1C1, multiplying the particular excitation codevector by an excitation gain factor to provide correlation with an energy of the representative electrical signal for each representative input reference signal vector.

17. A method for reconstructing a speech signal pattern in a digital speech coder, the signal being partitioned into successive time intervals, each time interval signal partition having a representative input reference signal with a set of vectors, and having at least a first representative electrical signal for each representative input reference signal of each time interval signal partition, the method utilizing at least a codebook unit having at least a codebook memory, a gain adjuster where selected, a synthesis unit having at least a first synthesis filter, a combiner, and a perceptual weighting unit having at least a first perceptual weighting filter, for utilizing the electrical signals of the representative input reference signals to at least generate a related set of synthesized signal vectors for reconstructing the signal, the method comprising the steps of:

(17A) utilizing the at least first representative electrical signal for each representative input reference signal for a time signal partition to obtain a set of uninterpolated parameters for the at least first synthesis filter;

(17B) utilizing the at least first synthesis filter to obtain the corresponding impulse response representation, and interpolating the impulse responses of each adjacent time signal partition and of a time signal partition immediately thereafter to provide a set of interpolated synthesis filters for desired subpartitions; and

utilizing the interpolated synthesis filters to provide a corresponding set of interpolated perceptual weighting filters for desired subpartitions;

such that smooth transitions of the synthesis filter and the perceptual weighting filter between each pair of adjacent partitions are obtained;

(17C) utilizing the set of input reference signal vectors, the related set of interpolated synthesis filters and the related set of interpolated perceptual weighting filters for the current time signal partition to select the corresponding set of optimal excitation codevectors from the at least first codebook memory, further implementing the following steps for each desired input reference signal vector;

(17C1) providing a particular excitation codevector which is associated with a particular index from the at least first codebook memory, the codebook memory having a set of excitation codevectors stored therein responsive to the representative input vectors;

(17C2) inputting the particular excitation codevector into the corresponding interpolated synthesis filter to produce the synthesized signal vector;

(17C3) subtracting the synthesized signal vector from the input reference signal vector related thereto to obtain a corresponding reconstruction error vector;

(17C4) inputting the reconstruction error vector into the corresponding interpolated perceptual weighting unit to determine a corresponding perceptually weighted squared error;

(17C5) determining and storing index of codevector having the perceptually weighted squared error smaller than all other errors produced by other codevectors;

(17C6) repeating the steps (17C1), (17C2), (17C3), (17C4), and (17C5), for every excitation codevector in the codebook memory and implementing these steps utilizing a fast codebook search method, to determine an optimal excitation codevector for producing the minimum weighted squared error among all excitation codevectors for the related input reference signal vector; and

(D) successively inputting the set of optimal excitation codevectors into the corresponding set of interpolated synthesis filters to produce the related set of synthesized signal vectors for the given input reference signal for reconstructing the input signal.

18. The method of claim 17, wherein the signal is a speech waveform.

19. The method of claim 17, wherein the at least first synthesis filter is at least a first time-varying linear predictive coding synthesis filter (LPC-SF).

20. The method of claim 19, wherein the at least first LPC-SF has a transfer function substantially of a form:

##EQU35## where a_i '"'"'s, for i=1,2, . . . , p represent a set of estimated prediction coefficients obtained by analyzing the corresponding time signal partition and p represents a predictor order.

21. The method of claim 20, wherein the interpolated synthesis filter is approximated by an all pole filter whose parameters are utilized in the LPC synthesis filter and in the perceptual weighting filter for interpolating subpartitions, wherein the all pole filter parameters are obtained utilizing the steps of:

truncating interpolated impulse samples;

estimating a first p+1 autocorrelation coefficients using truncated interpolated impulse response samples; and

converting the autocorrelation coefficients to direct form prediction coefficients using a recursion algorithm.

22. The method of claim 21, wherein the estimated autocorrelation coefficients at the m-th subpartition can be expressed as:

##EQU36## for k=0,1, . . . , p and the summation is over all available partition impulse responses, such that ##EQU37## are autocorrelation coefficients of uninterpolated impulse response of the adjacent and current partitions, and ##EQU38## and i,j=1,2 where i≠

j, are cross-correlation coefficients between the uninterpolated impulse responses.

23. The method of claim 17, wherein the LPC-SFs of a adjacent time signal partition and of a time partition immediately thereafter are substantially of a form:

##EQU39## where a_i.sup.(j) '"'"'s, for i=1, 2, 3, . . . , p and j=1,2 represent a set of prediction coefficients in an adjacent time signal partition when j=1 and of a current time signal partition immediately thereafter when j=2, respectively, p represents a predictor order such that

an impulse response for the transfer function H.sup.(j) (z) is substantially of a form ##EQU40## where ∂

(n) is a unit sample function, and such that the impulse response of the at least first synthesis filter at an m-th subpartition of a current time partition obtained through linear interpolation of h.sup.(1) (n) and h.sup.(2) (n) respectively, denoted below as h_m (n), is substantially of a form;

space="preserve" listing-type="equation">h.sub.m (n)=α

.sub.m h.sup.(1) (n)+β

.sub.m h.sup.(2) (n),

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition, thereby providing a transfer function of the interpolated synthesis filter substantially of a form;

##EQU41## wherein the perceptual weighting filter at the m-th subpartition of a current time interval signal partition has a transfer function of the form;

##EQU42## where γ

is typically selected to be substantially 0.8.

24. The method of claim 17, wherein the excitation code vectors are stored in memory.

25. The method of claim 17, wherein the perceptual weighting unit includes at least a first perceptual weighting filter having a transfer function substantially of a form:

##EQU43## where γ

is typically selected to be substantially 0.8.

26. The method of claim 17, wherein determining an optimal excitation codevector from the codebook memory for each input reference vector includes signal processing every excitation codevector in the codebook memory for each input reference vector, then determining the optimal excitation codevector of those codevectors processed.

27. The method of claim 17, wherein the fast codebook search method further includes utilizing a simplified method to obtain the perceptually weighted squared error between an input signal vector and a related synthesized codevector utilizing an i-th excitation codevector, denoting this error by E_i, such that:

##EQU44## where x represents an input target vector at a subpartition that is substantially equal to an input reference signal vector at a subpartition filtered by a corresponding interpolated weighting filter with a zero-input response of a corresponding interpolated weighted LPC-SF subtracted from it, A_i represents a dot product of the vector x and an i-th filtered codevector y_i,m at an m-th subpartition, and B_i represents the squared norm of the vector y_i,m.

28. The method of claim 27, wherein the corresponding interpolated weighted LPC-SF has a transfer function of H_m (z/γ

), such that;

##EQU45## where for an m-th subpartition, γ

is typically selected to be 0.8, and a_i,m, for i=1,2, . . . p, such that p is a predictor order, represent the parameters of corresponding interpolated LPC-SF,

the impulse response of H_m (z/γ

), h_wm (n), is substantially equal to;

space="preserve" listing-type="equation">h.sub.wm (n)=γ

.sup.n h.sub.m (n),

and where h_m (n) is an impulse response of corresponding LPC-SF,

utilizing a fact that h_m (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated LPC-SFs, h_wm (n), at each interpolating subpartition, determined in a fast codebook search as a linear interpolation of two impulse responses of related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">h.sub.wm (n)=α

.sub.m h.sub.w.sup.(1) (n)+β

.sub.m h.sub.w.sup.(2) (n),

where h_w.sup.(j) (n)=γ

ⁿ h.sup.(j) (n) for j=1,2 are exponentially weighted uninterpolated impulse responses of the previous, when j=1, and the current, when j=2, LPC synthesis filters, and where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

29. The method of claim 27, wherein the filtered codevector y_i,m is determined as a convolution of the i-th excitation codevector c_i with the corresponding weighted impulse response h_wm (n), the convolution substantially of a form:

y_i,m =F_wm c_i, where ##EQU46## and where k represents a dimension of a codevector, further utilizing the fact that h_wm (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated weighted LPC-SFs, the filtered codevector y_i,m at each interpolating subpartition may be determined as linear interpolation of two codevectors filtered by the related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">y.sub.i,m =α

.sub.m y.sub.i.sup.(1) +β

.sub.m y.sub.i.sup.(2),

and where y_i.sup.(j) =F_w.sup.(j) c_i for j=1,2 and where matrices F_w.sup.(1) and F_w.sup.(2) have a same format as the matrix F_wm, but with different elements h_w.sup.(1) (n) and h_w.sup.(2) (n), respectively.

30. The method of claim 27, wherein the squared norm B_i at each interpolating subpartition is a weighted sum of a squared norm of a filtered codevector y_i.sup.(1), a squared norm of the filtered codevector y_i.sup.(2), and a dot product of those two filtered codevectors, substantially being of a form:

space="preserve" listing-type="equation">B.sub.i =α

.sub.m.sup.2 ||y.sub.i.sup.(1) ||.sup.2 +β

.sub.m.sup.2 ||y.sub.i.sup.(2) ||.sup.2 +2α

.sub.m β

.sub.m <

y.sub.i.sup.(1) ·

y.sub.i.sup.(2) >

,

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

31. The method of claim 27, wherein determination of the dot product A_i for each interpolating subpartition comprises two steps:

32A) backward filtering such that z=F^t_wm x wherein ##EQU47## and where k represents a dimension of a codevector; and

where t represents a transpose operator; and

32B) forming a dot product such that;

space="preserve" listing-type="equation">A.sub.i =<

z·

c.sub.i >

,

where c_i is the ith excitation codevector.

32. The method of claim 17, further including, after step 17C1, multiplying the particular excitation codevector by an excitation gain factor to provide correlation with an energy of the representative electrical signal for each representative input reference signal vector.

33. A device for reconstructing a signal, the signal being partitioned into successive time intervals, each time interval signal partition having a representative input reference signal with a set of vectors, and having at least a first representative electrical signal for each representative input reference signal of each time interval signal partition, for utilizing the electrical signals of the representative input reference signals to at least generate a related set of synthesized signal vectors for reconstructing the signal, the device comprising at least:

(33A) a first synthesis unit, responsive to the at least first representative electrical signal for each representative input reference signal, for utilizing the at least first representative electrical signal for each representative input reference signal for a time signal partition to obtain a set of uninterpolated parameters for the at least first synthesis filter and the impulse response of this synthesis filter, and for interpolating the impulse responses of each adjacent time signal partition and of a current time signal partition immediately thereafter to provide a set of interpolated synthesis filters for desired subpartitions; and

utilizing the interpolated synthesis filters to provide a corresponding set of interpolated perceptual weighting filters to at least a first perceptual weighting unit for desired subpartitions such that the at least first perceptual weighting unit provides at least a first perceptually weighted squared error and such that smooth transitions of the synthesis filter and the perceptual weighting filter between each pair of adjacent partitions are obtained;

(33B) a codebook unit, responsive to the set of input reference signal vectors, the related set of interpolated synthesis filters and the related set of interpolated perceptual weighting filters for the current time signal partition, for selecting the corresponding set of optimal excitation codevectors from the at least first codebook memory for each desired input reference signal vector, further comprising at least;

(33B1) a codebook memory, for providing a particular excitation codevector which is associated with a particular index from the at least first codebook memory, the codebook memory having a set of excitation codevectors stored therein responsive to the representative input vectors;

(33B2) an interpolated synthesis filter having a transfer function, responsive to the particular excitation codevector for producing a synthesized signal vector;

(33B3) a combiner, responsive to the synthesized signal vector and to the input reference signal vector related thereto, for subtracting the synthesized signal vector from the input reference signal vector related thereto to obtain a corresponding reconstruction error vector;

(33B4) an interpolated perceptual weighting unit, responsive to the corresponding reconstruction error vector and to the interpolated synthesis filter transfer function, for determining a corresponding perceptually weighted squared error;

(33B5) a selector, responsive to the corresponding perceptually weighted squared error for determining and storing an index of a codevector having the perceptually weighted squared error smaller than all other errors produced by other codevectors;

(33B6) repetition means, responsive to the number of excitation codevectors in the codebook memory, for repeating the steps (33B1), (33B2), (33B3), (33B4), and (33B5) for every excitation codevector in the codebook memory and for implementing these steps utilizing a fast codebook search method, to determine an optimal excitation codevector for producing the minimum weighted squared error among all excitation codevectors for the related input reference signal vector; and

(33C) codebook unit control means, responsive to the set of optimal excitation codevectors for successively inputting the set of optimal excitation codevectors into the corresponding set of interpolated synthesis filters to produce the related set of synthesized signal vectors for the given input reference signal for reconstructing the input signal.

34. The device of claim 33, wherein the signal is a speech waveform.

35. The device of claim 33, wherein the at least first synthesis filter is at least a first time-varying linear predictive coding synthesis filter (LPC-SF).

36. The device of claim 35, wherein the at least first LPC-SF has a transfer function substantially of a form:

##EQU48## where a_i '"'"'s, for i=1,2, . . . , p represent a set of estimated prediction coefficients obtained by analyzing the corresponding time signal partition and p represents a predictor order.

37. The device of claim 33, wherein the LPC-SFs of a adjacent time signal partition and of a time partition immediately thereafter are substantially of a form:

##EQU49## where a_i.sup.(j) '"'"'s, for i=1, 2, 3, . . . , p and j=1,2 represent a set of prediction coefficients in a adjacent time signal partition when j=1 and of a current time signal partition immediately thereafter when j=2, respectively, p represents a predictor order such that

an impulse response for the transfer function H.sup.(j) (z) is substantially of a form ##EQU50## where ∂

(n) is a unit sample function, and such that the impulse response of the at least first synthesis filter at an m-th subpartition of a current time partition obtained through linear interpolation of h.sup.(1) (n) and h.sup.(2) (n) respectively, denoted below as h_m (n), is substantially of a form;

space="preserve" listing-type="equation">h.sub.m (n)=α

.sub.m h.sup.(1) (n)+β

.sub.m h.sup.(2) (n),

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition, thereby providing a transfer function of the interpolated synthesis filter substantially of a form;

##EQU51## wherein the perceptual weighting filter at the m-th subpartition of a current time interval signal partition has a transfer function of the form;

##EQU52## where γ

is typically selected to be substantially 0.8.

38. The device of claim 37, wherein the interpolated synthesis filter is approximated by an all pole filter whose parameters are utilized in the LPC synthesis filter and in the perceptual weighting filter for interpolating subpartitions, wherein the all pole filter parameters are obtained utilizing at least:

estimating means, responsive to interpolated impulse response samples, for truncating interpolated impulse samples and estimating a first p+1 autocorrelation coefficients using truncated interpolated impulse response samples; and

converting means, responsive to the estimated autocorrelation coefficients, for converting the autocorrelation coefficients to direct form prediction coefficients using a recursion algorithm.

39. The device of claim 38, wherein the estimated autocorrelation coefficients at the m-th subpartition can be expressed as:

##EQU53## for k=0,1, . . . , p and the summation is over all available partition impulse responses, such that ##EQU54## are autocorrelation coefficients of uninterpolated impulse response of the adjacent and current partitions, and ##EQU55## and i,j=1,2 where i≠

j, are cross-correlation coefficients between the uninterpolated impulse responses.

40. The device of claim 33, wherein the excitation code vectors are stored in memory.

41. The device of claim 33, wherein the perceptual weighting unit includes at least a first perceptual weighting filter having a transfer function substantially of a form:

##EQU56## where γ

is typically selected to be substantially 0.8.

42. The device of claim 33, wherein determining an optimal excitation codevector from the codebook memory for each input reference vector includes signal processing every excitation codevector in the codebook memory for each input reference vector, then determining the optimal excitation codevector of those codevectors processed.

43. The device of claim 33, wherein the fast codebook search device further includes utilizing a simplified method to obtain the perceptually weighted squared error between an input signal vector and a related synthesized codevector utilizing an i-th excitation codevector, denoting this error by E_i, such that:

##EQU57## where x represents an input target vector at a subpartition that is substantially equal to an input reference signal vector at a subpartition filtered by a corresponding interpolated weighting filter with a zero-input response of a corresponding interpolated weighted LPC-SF subtracted from it, A_i represents a dot product of the vector x and an i-th filtered codevector y_i,m at an m-th subpartition, and B_i represents the squared norm of the vector y_i,m.

44. The device of claim 43, wherein the corresponding interpolated weighted LPC-SF has a transfer function of H_m (z/γ

), such that;

##EQU58## where for an m-th subpartition, γ

is typically selected to be 0.8, and a_i,m, for i=1,2, . . . p, such that p is a predictor order, represent the parameters of corresponding interpolated LPC-SF,

the impulse response of H_m (z/γ

), h_wm (n), is substantially equal to;

space="preserve" listing-type="equation">h.sub.wm (n)=γ

.sup.n h.sub.m (n),

and where h_m (n) is an impulse response of corresponding LPC-SF,

utilizing a fact that h_m (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated LPC-SFs, h_wm (n), at each interpolating subpartition, determined in a fast codebook search as a linear interpolation of two impulse responses of related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">h.sub.wm (n)=α

.sub.m h.sub.w.sup.(1) (n)+β

.sub.m h.sub.w.sup.(2) (n),

where h_w.sup.(j) (n)=γ

ⁿ h.sup.(j) (n) for j=1,2 are exponentially weighted uninterpolated impulse responses of the previous, when j=1, and the current, when j=2, LPC synthesis filters, and where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

45. The device of claim 43, wherein the filtered codevector y_i,m is determined as a convolution of the i-th excitation codevector c_i with the corresponding weighted impulse response h_wm (n), the convolution being:

y_i,m =F_wm c_i, where ##EQU59## and where k represents a dimension of a codevector, further utilizing the fact that h_wm (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated weighted LPC-SFs, the filtered codevector y_i,m at each interpolating subpartition may be determined as linear interpolation of two codevectors filtered by the related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">y.sub.i,m =α

.sub.m y.sub.i.sup.(1) +β

.sub.m y.sub.i.sup.(2),

and where y_i.sup.(j) =F_w.sup.(j) c_i for j=1,2 and where matrices F_w.sup.(1) and F_w.sup.(2) have a same format as the matrix F_wm, but with different elements h_w.sup.(1) (n) and h_w.sup.(2) (n), respectively.

46. The device of claim 43, further including a second determiner, responsive to the squared norm of a filtered codevector y_i.sup.(1), the squared norm of the filtered codevector y_i.sup.(2), and a dot product of those two filtered codevectors, for determining the squared norm B_i at each interpolating subpartition, a weighted sum of a squared norm of a filtered codevector y_i.sup.(1), a squared norm of the filtered codevector y_i.sup.(2), and a dot product of those two filtered codevectors, substantially being of a form:

space="preserve" listing-type="equation">B.sub.i =α

.sub.m.sup.2 ||y.sub.i.sup.(1) ||.sup.2 +β

.sub.m.sup.2 ||y.sub.i.sup.(2) ||.sup.2 +2α

.sub.m β

.sub.m <

y.sub.i.sup.(1) ·

y.sub.i.sup.(2) >

,

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

47. The device of claim 43, further including a first determiner for determination of the dot product A_i for each interpolating subpartition comprising at least:

48A) a backward filter, responsive to an input vector x and to the matrix F_wm, wherein ##EQU60## and where k represents a dimension of a codevector, for determining a vector z such that

space="preserve" listing-type="equation">z=F.sup.t.sub.wm x; and

where t represents a transpose operator; and

48B) a dot product determiner, responsive to the vector z and to the m-th excitation codevector, for forming a dot product such that;

space="preserve" listing-type="equation">A.sub.i =<

z·

c.sub.i >

,

where c_i is the ith excitation codevector.

48. The device of claim 33, further including a gain adjuster, responsive to the particular excitation codevector, for multiplying the particular excitation codevector (provided by the codebook memory) by an excitation gain factor to provide correlation with an energy of the representative electrical signal for each representative input reference signal vector.

49. A device for reconstructing a speech signal in a digital speech coder, the signal being partitioned into successive time intervals, each time interval signal partition having a representative input reference signal with a set of vectors, and having at least a first representative electrical signal for each representative input reference signal of each time interval signal partition, for utilizing the electrical signals of the representative input reference signals to at least generate a related set of synthesized signal vectors for reconstructing the signal, the device comprising at least:

(49A) a first synthesis unit, responsive to the at least first representative electrical signal for each representative input reference signal, for utilizing the at least first representative electrical signal for each representative input reference signal for a time signal partition to obtain a set of uninterpolated parameters for the at least first synthesis filter and the impulse response of this synthesis filter, and for interpolating the impulse responses of each adjacent time signal partition and of a current time signal partition immediately thereafter to provide a set of interpolated synthesis filters for desired subpartitions; and

utilizing the interpolated synthesis filters to provide a corresponding set of interpolated perceptual weighting filters to at least a first perceptual weighting unit for desired subpartitions such that the at least first perceptual weighting unit provides at least a first perceptually weighted squared error and such that smooth transitions of the synthesis filter and the perceptual weighting filter between each pair of adjacent partitions are obtained;

(49B) a codebook unit, responsive to the set of input reference signal vectors, the related set of interpolated synthesis filters and the related set of interpolated perceptual weighting filters for the current time signal partition, for selecting the corresponding set of optimal excitation codevectors from the at least first codebook memory for each desired input reference signal vector, further comprising at least;

(49B1) a codebook memory, for providing a particular excitation codevector which is associated with a particular index from the at least first codebook memory, the codebook memory having a set of excitation codevectors stored therein responsive to the representative input vectors;

(49B2) an interpolated synthesis filter having a transfer function, responsive to the particular excitation codevector for producing a synthesized signal vector;

(49B3) a combiner, responsive to the synthesized signal vector and to the input reference signal vector related thereto, for subtracting the synthesized signal vector from the input reference signal vector related thereto to obtain a corresponding reconstruction error vector;

(49B4) an interpolated perceptual weighting unit, responsive to the corresponding reconstruction error vector and to the interpolated synthesis filter transfer function, for determining a corresponding perceptually weighted squared error;

(49B5) a selector, responsive to the corresponding perceptually weighted squared error for determining and storing an index of a codevector having the perceptually weighted squared error smaller than all other errors produced by other codevectors;

(49B6) repetition means, responsive to the number of excitation codevectors in the codebook memory, for repeating the steps (49B1), (49B2), (49B3), (49B4), and (49B5) for every excitation codevector in the codebook memory and for implementing these steps utilizing a fast codebook search method, to determine an optimal excitation codevector for producing the minimum weighted squared error among all excitation codevectors for the related input reference signal vector; and

(D) codebook unit control means, responsive to the set of optimal excitation codevectors for successively inputting the set of optimal excitation codevectors into the corresponding set of interpolated synthesis filters to produce the related set of synthesized signal vectors for the given input reference signal for reconstructing the input signal.

50. The device of claim 49, wherein the at least first synthesis filter is at least a first time-varying linear predictive coding synthesis filter (LPC-SF).

51. The device of claim 50, wherein the at least first LPC-SF has a transfer function substantially of a form:

##EQU61## where a_i '"'"'s, for i=1,2, . . . , p represent a set of estimated prediction coefficients obtained by analyzing the corresponding time signal partition and p represents a predictor order.

52. The device of claim 50, wherein the LPC-SFs of an adjacent time signal partition and of a time partition immediately thereafter are substantially of a form:

##EQU62## where a_i.sup.(j) '"'"'s, for i=1, 2, 3, . . . , p and j=1, 2 represent a set of prediction coefficients in an adjacent time signal partition when j=1 and of a current time signal partition immediately thereafter when j=2, respectively, p represents a predictor order such that

an impulse response for the transfer function H.sup.(j) (z) is substantially of a form ##EQU63## where ∂

(n) is a unit sample function, and such that the impulse response of the at least first synthesis filter at an m-th subpartition of a current time partition obtained through linear interpolation of h.sup.(1) (n) and h.sup.(2) (n) respectively, denoted below as h_m (n), is substantially of a form;

space="preserve" listing-type="equation">h.sub.m (n)=α

.sub.m h.sup.(1) (n)+β

.sub.m h.sup.(2) (n),

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition, thereby providing a transfer function of the interpolated synthesis filter substantially of a form;

##EQU64## wherein the perceptual weighting filter at the m-th subpartition of a current time interval signal partition has a transfer function of the form;

##EQU65## where γ

is typically selected to be substantially 0.8.

53. The device of claim 52, wherein the interpolated synthesis filter is approximated by an all pole filter whose parameters are utilized in the LPC synthesis filter and in the perceptual weighting filter for interpolating subpartitions, wherein the all pole filter parameters are obtained utilizing at least:

estimating means, responsive to interpolated impulse response samples, for truncating interpolated impulse samples and estimating a first p+1 autocorrelation coefficients using truncated interpolated impulse response samples; and

converting means, responsive to the estimated autocorrelation coefficients, for converting the autocorrelation coefficients to direct form prediction coefficients using a recursion algorithm.

54. The device of claim 53, wherein the estimated autocorrelation coefficients at the m-th subpartition can be expressed as:

##EQU66## for k=0,1, . . . , p and the summation is over all available partition impulse responses, such that ##EQU67## are autocorrelation coefficients of uninterpolated impulse response of the adjacent and current partitions, and ##EQU68## and i,j=1,2 where i≠

j, are cross-correlation coefficients between the uninterpolated impulse responses.

55. The device of claim 49, wherein the excitation code vectors are stored in memory.

56. The device of claim 49, wherein the perceptual weighting unit includes at least a first perceptual weighting filter having a transfer function substantially of a form:

##EQU69## where γ

is typically selected to be substantially 0.8.

57. The device of claim 49, wherein determining an optimal excitation codevector from the codebook memory for each input reference vector includes signal processing every excitation codevector in the codebook memory for each input reference vector, then determining the optimal excitation codevector of those codevectors processed.

58. The device of claim 49, wherein the fast codebook search device further includes codebook unit means for utilizing a simplified method to obtain the perceptually weighted squared error between an input signal vector and a related synthesized codevector utilizing an i-th excitation codevector, denoting this error by E_i, such that:

##EQU70## where x represents an input target vector at a subpartition that is substantially equal to an input reference signal vector at a subpartition filtered by a corresponding interpolated weighting filter with a zero-input response of a corresponding interpolated weighted LPC-SF subtracted from it, A_i represents a dot product of the vector x and an i-th filtered codevector y_i,m at an m-th subpartition, and B_i represents the squared norm of the vector y_i,m.

59. The device of claim 58, wherein the corresponding interpolated weighted LPC-SF has a transfer function of H_m (z/γ

), such that;

##EQU71## where for an m-th subpartition, γ

is typically selected to be 0.8, and a_i,m, for i=1,2, . . . p, such that p is a predictor order, represent the parameters of corresponding interpolated LPC-SF,

the impulse response of H_m (z/γ

), h_wm (n), is substantially equal to;

space="preserve" listing-type="equation">h.sub.wm (n)=γ

.sup.n h.sub.m (n),

and where h_m (n) is an impulse response of corresponding LPC-SF,

utilizing a fact that h_m (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated LPC-SFs, h_wm (n), at each interpolating subpartition, determined in a fast codebook search as a linear interpolation of two impulse responses of related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">h.sub.wm (n)=α

.sub.m h.sub.w.sup.(1) (n)+β

.sub.m h.sub.w.sup.(2) (n),

where h_w.sup.(j) (n)=γ

ⁿ h.sup.(j) (n) for j=1,2 are exponentially weighted uninterpolated impulse responses of the previous, when j=1, and the current, when j=2, LPC synthesis filters, and where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

60. The device of claim 58, wherein the filtered codevector y_i,m is determined as a convolution of the i-th excitation codevector c_i with the corresponding weighted impulse response h_wm (n), the convolution substantially of a form:

y_i,m =F_wm c_i, where ##STR3## and where k represents a dimension of a codevector, further utilizing the fact that h_wm (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated weighted LPC-SFs, the filtered codevector y_i,m at each interpolating subpartition may be determined as linear interpolation of two codevectors filtered by the related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">y.sub.i,m =α

.sub.m y.sub.i.sup.(1) +β

.sub.m y.sub.i.sup.(2),

and where y_i.sup.(j) =F_w.sup.(j) c_i for j=1,2 and where matrices F_w.sup.(1) and F_w.sup.(2) have a same format as the matrix F_wm, but with different elements h_w.sup.(1) (n) and h_w.sup.(2) (n), respectively.

61. The device of claim 58, further including a second determiner, responsive to the squared norm of a filtered codevector y_i.sup.(1), the squared norm of the filtered codevector y_i.sup.(2), and a dot product of those two filtered codevectors, for determining the squared norm B_i at each interpolating subpartition, a weighted sum of a squared norm of a filtered codevector y_i.sup.(1), the weighted squared norm of the filtered codevector y_i.sup.(2), and a dot product of those two filtered codevectors, substantially being of a form:

space="preserve" listing-type="equation">B.sub.i =α

.sub.m.sup.2 ||y.sub.i.sup.(1) ||.sup.2 +β

.sub.m.sup.2 ||y.sub.i.sup.(2) ||.sup.2 +2α

.sub.m β

.sub.m <

y.sub.i.sup.(1) ·

y.sub.i.sup.(2) >

,

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

62. The device of claim 58, further including a first determiner for determination of the dot product A_i for each interpolating subpartition comprising at least:

63A) a backward filter, responsive to an input vector x and to the matrix F_wm wherein ##STR4## and where k represents a dimension of a codevector, for determining a vector z such that z=F^t_wm x; and

where t represents a transpose operator; and

63B) a dot product determiner, responsive to the vector z and to the m-th excitation codevector, for forming a dot product such that;

space="preserve" listing-type="equation">A.sub.i =<

z·

c.sub.i >

,

where c_i is the ith excitation codevector.

63. The device of claim 49, further including a gain adjuster, responsive to the particular excitation codevector provided by the codebook memory, for multiplying the particular excitation codevector by an excitation gain factor to provide correlation with an energy of the representative electrical signal for each representative input reference signal vector.

64. A system for reconstructing a speech signal in a digital speech coder, the signal being partitioned into successive time intervals, each time interval signal partition having a representative input reference signal with a set of vectors, and having at least a first representative electrical signal for each representative input reference signal of each time interval signal partition, for utilizing the electrical signals of the representative input reference signals to at least generate a related set of synthesized signal vectors for reconstructing the signal, the system comprising at least:

(64A) a first synthesis unit, responsive to the at least first representative electrical signal for each representative input reference signal, for utilizing the at least first representative electrical signal for each representative input reference signal for a time signal partition to obtain a set of uninterpolated parameters for the at least first synthesis filter and the impulse response of this synthesis filter, and having a first synthesis filter, the at least first synthesis filter being at least a first time-varying linear predictive coding synthesis filter (LPC-SF) wherein the at least first LPC-SF has a transfer function substantially of a form;

##EQU72## where a_i '"'"'s, for i=1,2, . . . , p represent a set of estimated prediction coefficients obtained by analyzing the corresponding time signal partition and p represents a predictor order, responsive to the set of uninterpolated parameters, for obtaining the corresponding impulse response representation, and interpolating the impulse responses of each adjacent time signal partition and of a current time signal partition immediately thereafter, wherein the LPC-SFs of a adjacent time signal partition and of a time partition immediately thereafter are substantially of a form;

##EQU73## where a_i.sup.(j) '"'"'s, for i=1, 2, 3, . . . , p and j=1, 2 represent a set of prediction coefficients in an adjacent time signal partition when j=1 and of a current time signal partition immediately thereafter when j=2, respectively, p represents a predictor order such that

an impulse response for the transfer function H.sup.(j) (z) is substantially of a form ##EQU74## where ∂

(n) is a unit sample function, and such that the impulse response of the at least first synthesis filter at an m-th subpartition of a current time partition obtained through linear interpolation of h.sup.(1) (n) and h.sup.(2) (n) respectively, denoted below as h_m (n), is substantially of a form;

space="preserve" listing-type="equation">h.sub.m (n)=α

.sub.m h.sup.(1) (n)+β

.sub.m h.sup.(2) (n),

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition, thereby providing a transfer function of an interpolated synthesis filter substantially of a form;

##EQU75## wherein the perceptual weighting filter at the m-th subpartition of a current time interval signal partition has a transfer function of the form;

##EQU76## where γ

is typically selected to be substantially 0.8, to provide a set of interpolated synthesis filters for desired subpartitions; and

utilizing the interpolated synthesis filters, to provide a corresponding set of interpolated perceptual weighting filters to at least a first perceptual weighting unit for desired subpartitions such that the at least first perceptual weighting unit provides at least a first perceptually weighted squared error and such that smooth transitions of the synthesis filter and the perceptual weighting filter between each pair of adjacent partitions are obtained;

(64B) a codebook unit, responsive to the set of input reference signal vectors, the related set of interpolated synthesis filters and the related set of interpolated perceptual weighting filters for the current time signal partition, for selecting the corresponding set of optimal excitation codevectors from the at least first codebook memory for each desired input reference signal vector, further comprising at least;

(64B1) a first codebook memory, for providing a particular excitation codevector which is associated with a particular index from the at least first codebook memory, the codebook memory having a set of excitation codevectors stored therein responsive to the representative input vectors;

(64B2) an interpolated synthesis filter having a transfer function, responsive to the particular excitation codevector for producing a synthesized signal vector;

(64B3) a combiner, responsive to the synthesized signal vector and to the input reference signal vector related thereto, for subtracting the synthesized signal vector from the input reference signal vector related thereto to obtain a corresponding reconstruction error vector;

(64B4) an interpolated perceptual weighting unit, responsive to the corresponding reconstruction error vector and to the interpolated synthesis filter transfer function, for determining a corresponding perceptually weighted squared error;

(64B5) a selector, responsive to the corresponding perceptually weighted squared error for determining and storing an index of a codevector having the perceptually weighted squared error smaller than all other errors produced by other codevectors;

(64B6) repetition means, responsive to the number of excitation codevectors in the codebook memory, for repeating the steps (64B1), (64B2), (64B3), (64B4), and (64B5) for every excitation codevector in the codebook memory and for implementing these steps utilizing a fast codebook search method, to determine an optimal excitation codevector for producing the minimum weighted squared error among all excitation codevectors for the related input reference signal vector; and

(C) codebook unit control means, responsive to the set of optimal excitation codevectors for successively inputting the set of optimal excitation codevectors into the corresponding set of interpolated synthesis filters to produce the related set of synthesized signal vectors for the given input reference signal for reconstructing the input signal.

65. The system of claim 64, wherein the synthesis filter is approximated by an all pole synthesis filter that is utilized to provide parameters for interpolating subpartitions in the LPC-SF filter and in the perceptual weighting filter, wherein the all pole synthesis filter parameters are obtained utilizing at least:

estimating means, responsive to interpolated impulse response samples, for truncating interpolated impulse samples and estimating a first p+1 autocorrelation coefficients using truncated interpolated impulse response samples; and

converting means, responsive to the estimated autocorrelation coefficients, for converting the autocorrelation coefficients to direct form prediction coefficients using a recursion algorithm.

66. The system of claim 65, wherein the estimated autocorrelation coefficients at the m-th subpartition can be expressed as:

##EQU77## for k=0,1, . . . , p and the summation is over all available partition impulse responses, such that ##EQU78## are autocorrelation coefficients of uninterpolated impulse response of the adjacent and current partitions, and ##EQU79## and i,j=1,2 where i≠

j, are cross-correlation coefficients between the uninterpolated impulse responses.

67. The system of claim 64, wherein the excitation code vectors are stored in memory.

68. The system of claim 64, wherein the perceptual weighting unit includes at least a first perceptual weighting filter having a transfer function substantially of a form:

##EQU80## where γ

is typically selected to be substantially 0.8.

69. The system of claim 64, wherein determining an optimal excitation codevector from the codebook memory for each input reference vector includes signal processing every excitation codevector in the codebook memory for each input reference vector, then determining the optimal excitation codevector of those codevectors processed.

70. The system of claim 64, wherein the fast codebook search system further includes utilizing a simplified method to obtain the perceptually weighted squared error between an input signal vector and a related synthesized codevector utilizing an i-th excitation codevector, denoting this error by E_i, such that:

##EQU81## where x represents an input target vector at a subpartition that is substantially equal to an input reference signal vector at a subpartition filtered by a corresponding interpolated weighting filter with a zero-input response of a corresponding interpolated weighted LPC-SF subtracted from it, A_i represents a dot product of the vector x and an i-th filtered codevector y_i,m at an m-th subpartition, and B_i represents the squared norm of the vector y_i,m.

71. The system of claim 70, wherein the corresponding interpolated weighted LPC-SF has a transfer function of H_m (z/γ

), such that;

##EQU82## where for an m-th subpartition, γ

is typically selected to be 0.8, and a_i,m, for i=1,2, . . . p, such that p is a predictor order, represent the parameters of corresponding interpolated LPC-SF,

the impulse response of H_m (z/γ

), h_wm (n), is substantially equal to;

space="preserve" listing-type="equation">h.sub.wm (n)=γ

.sup.n h.sub.m (n),

and where h_m (n) is an impulse response of corresponding LPC-SF,

utilizing a fact that h_m (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated LPC-SFs, h_wm (n), at each interpolating subpartition, determined in a fast codebook search as a linear interpolation of two impulse responses of related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">h.sub.wm (n)=α

.sub.m h.sub.w.sup.(1) (n)+β

.sub.m h.sub.w.sup.(2) (n),

where h_w.sup.(j) (n)=γ

ⁿ h.sup.(j) (n) for j=1,2 are exponentially weighted uninterpolated impulse responses of the previous, when j=1, and the current, when j=2, LPC synthesis filters, and where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

72. The system of claim 70, wherein the filtered codevector y_i,m is determined as a convolution of the i-th excitation codevector c_i with the corresponding weighted impulse response h_wm (n), the convolution substantially of a form:

y_i,m =F_wm c_i, where ##STR5## and where k represents a dimension of a codevector, further utilizing the fact that h_wm (n) is a linear interpolation of the impulse responses of related previous and current uninterpolated weighted LPC-SFs, the filtered codevector y_i,m at each interpolating subpartition may be determined as linear interpolation of two codevectors filtered by the related previous and current uninterpolated weighted LPC-SFs;

space="preserve" listing-type="equation">y.sub.i,m =α

.sub.m y.sub.i.sup.(1) +β

.sub.m y.sub.i.sup.(2),

and where y_i.sup.(j) =F_w.sup.(j) c_i for j=1,2 and where matrices F_w.sup.(1) and F_w.sup.(2) have a same format as the matrix F_wm, but with different elements h_w.sup.(1) (n) and h_w.sup.(2) (n), respectively.

73. The system of claim 70, further including a second determiner, responsive to the squared norm of a filtered codevector y_i.sup.(1), the squared norm of the filtered codevector y_i.sup.(2), and a dot product of those two filtered codevectors, for determining the squared norm B_i at each interpolating subpartition, a weighted sum of a squared norm of a filtered codevector y_i.sup.(1), the squared norm of the filtered codevector y_i.sup.(2), and a dot product of those two filtered codevectors, substantially of a form:

space="preserve" listing-type="equation">B.sub.i =α

.sub.m.sup.2 ||y.sub.i.sup.(1) ||.sup.2 +β

.sub.m.sup.2 ||y.sub.i.sup.(2) ||.sup.2 +2α

.sub.m β

.sub.m <

y.sub.i.sup.(1) ·

y.sub.i.sup.(2) >

,

where β

_m =1-α

_m and 0<

α

_m <

1, where a different α

_m is utilized for each subpartition.

74. The system of claim 70, further including a first determiner for determination of the dot product A_i for each interpolating subpartition comprising at least:

75A) a backward filter, responsive to an input vector x and to the matrix F_wm, wherein ##STR6## and where k represents a dimension of a codevector, for determining a vector z such that

z=F^t_wm x; and

where t represents a transpose operator; and

75B) a dot product determiner, responsive to the vector z and to the m-th excitation codevector, for forming a dot product such that;

space="preserve" listing-type="equation">A.sub.i =<

z·

c.sub.i >

,

where c_i is the ith excitation codevector.

75. The system of claim 64, further including a gain adjuster, responsive to the particular excitation codevector provided by the codebook memory, for multiplying the particular excitation codevector by an excitation gain factor to provide correlation with an energy of the representative electrical signal for each representative input reference signal vector.