Patent Abstract:
A speech signal is encoded using code excited linear prediction for use in transmitting the speech signal to a receiver. The speech signal is sampled. A current sample of the speech signal is predicted based on in part a previous sample. An innovation sequence is determined based on in part a prediction error between the predicted current sample and the current sample of the speech signal. A code from each of a plurality of codebooks is selected. A combination of the selected codes is the determined innovation sequence. An index of the selected codes is identified and transmitted to the receiver. The transmitted index enables reconstruction of the speech signal at the receiver.

Full Description:
CROSS REFERENCES TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 08/734,356, filed Oct. 21, 1996, now U.S. Pat. No. 6,240,382 which is a continuation of U.S. application Ser. No. 08/166,223, filed Dec. 14, 1993, now U.S. Pat. No. 5,621,852. 
    
    
     FIELD OF THE INVENTION 
     This invention relates to digital speech encoders using code excited linear prediction coding, or CELP. More particularly, this invention relates a method and apparatus for efficiently selecting a desired codevector used to reproduce an encoded speech segment at the decoder. 
     BACKGROUND OF THE INVENTION 
     Direct quantization of analog speech signals is too inefficient for effective bandwidth utilization. A technique known as linear predictive coding, or LPC, which takes advantage of speech signal redundancies, requires much fewer bits to transmit or store speech signals. Originally speech signals are produced as a result of acoustical excitation of the vocal tract. While the vocal cords produce the acoustical excitation, the vocal tract (e.g. mouth, tongue and lips) acts as a time varying filter of the vocal excitation. Thus, speech signals can be efficiently represented as a quasi-periodic excitation signal plus the time varying parameters of a digital filter. In addition, the periodic nature of the vocal excitation can further be represented by a linear filter excited by a noise-like Gaussian sequence. Thus, in CELP, a first long delay predictor corresponds to the pitch periodicity of the human vocal cords, and a second short delay predictor corresponds to the filtering action of the human vocal tract 
     CELP reproduces the individual speaker&#39;s voice by processing the input speech to determine the desired excitation sequence and time varying digital filter parameters. At the encoder, a prediction filter forms an estimate for the current sample of the input signal based on the past reconstructed values of the signal at the receiver decoder, i.e. the transmitter encoder predicts the value that the receiver decoder will reconstruct. The difference between the current value and predicted value of the input signal is the prediction error. For each frame of speech, the prediction residual and filter parameters are communicated to the receiver. The prediction residual or prediction error is also known as the innovation sequence and is used at the receiver as the excitation input to the prediction filters to reconstruct the speech signal. Each sample of the reconstructed speech signal is produced by adding the received signal to the predicted estimate of the present sample. For each successive speech frame, the innovation sequence and updated filter parameters are communicated to the receiver decoder. 
     The innovation sequence is typically encoded using codebook encoding. In codebook encoding, each possible innovation sequence is stored as an entry in a codebook and each is represented by an index. The transmitter and receiver both have the same codebook contents. To communicate an given innovation sequence, the index for that innovation sequence in the transmitter codebook is transmitted to the receiver. At the receiver, the received index is used to look up the desired innovation sequence in the receiver codebook for use as the excitation sequence to the time varying digital filters. 
     The task of the CELP encoder is to generate the time varying filter coefficients and the innovation sequence in real time. The difficulty of rapidly selecting the best innovation sequence from a set of possible innovation sequences for each frame of speech is an impediment to commercial achievement of real time CELP based systems, such as cellular telephone, voice mail and the like. 
     Both random and deterministic codebooks are known. Random codebooks are used because the probability density function of the prediction error samples has been shown to be nearly white Gaussian random noise. However, random codebooks present a heavy computational burden to select an innovation sequence from the codebook at the encoder since the codebook must be exhaustively searched. 
     To select an innovation sequence from the codebook of stored innovation sequences, a given fidelity criterion is used. Each innovation sequence is filtered through time varying linear recursive filters to reconstruct (predict) the speech frame as it would be reconstructed at the receiver. The predicted speech frame using the candidate innovation sequence is compared with the desired target speech frame (filtered through a perceptual weighting filter) and the fidelity criterion is calculated. The process is repeated for each stored innovation sequence. The innovation sequence that maximizes the fidelity criterion function is selected as the optimum innovation sequence, and an index representing the selected optimum sequence is sent to the receiver, along with other filter parameters. 
     At the receiver, the index is used to access the selected innovation sequence, and, in conjunction with the other filter parameters, to reconstruct the desired speech. 
     The central problem is how to select an optimum innovation sequence from the codebook at the encoder within the constraints of real time speech encoding and acceptable transmission delay. In a random codebook, the innovation sequences are independently generated random white Gaussian sequences. The computational burden of performing an exhaustive search of all the innovation sequences in the random code book is extremely high because each innovation sequence must be passed through the prediction filters. 
     One prior art solution to the problem of selecting an innovation-sequence is found in U.S. Pat. No. 4,797,925 in which the adjacent codebook entries have a subset of elements in common. In particular, each succeeding code sequence may be generated from the previous code sequence by removing one or more elements from the beginning of the previous sequence and adding one or more elements to the end of the previous sequence. The filter response to each succeeding code sequence is then generated from the filter response to the preceding code sequence by subtracting the filter response to the first samples and appending the filter response to the added samples. Such overlapping codebook structure permits accelerated calculation of the fidelity criterion. 
     Another prior art solution to the problem of rapidly selecting an optimum innovation sequence is found in U.S. Pat. No. 4,817,157 in which the codebook of excitation vectors is derived from a set of M basis vectors which are used to generate a set of 2 M  codebook excitation code vectors. The entire codebook of 2 M  possible excitation vectors is searched using the knowledge of how the code vectors are generated from. the basis vectors, without having to generate and evaluate each of the individual code vectors. 
     SUMMARY OF THE INVENTION 
     The present invention is embodied in a speech communication system using a ternary innovation codebook which is formed by the sum of two binary codebooks. The ternary codebook has code sequences C k , constructed from the set of values, {−1,0,1}. To form the ternary codebook, one binary codebook has the values {0,1}, and the other binary codebook has the values {−1,0}. The sum of one binary codevector from each binary codebook forms a ternary codevector. The codebook structure of the present invention, permits several efficient search procedures and reduced storage. For example, a ternary codebook of 256 sequences may be formed from two binary codebooks of 16 each (32 total). Each of the 256 ternary sequences is formed as the sum of 1 of 16 binary sequences from the first binary codebook and 1 of 16 binary sequences from the second binary codebook. 
     More important than reduced storage, the binary codebooks may be efficiently searched for optimum values of a given fidelity criterion function. The computational burden of searching for optimum sequences is eased because there are fewer sequences (32 verses 256 in the above example) to filter and correlate in computing the fidelity criterion function, even for an exhaustive search of all combinations of the two binary codebooks. Since the processing is linear, the principle of superposition may be used to obtain the result of ternary codevector processing by adding the results of binary codevector processing. In addition, as alternate embodiments to an exhaustive search of the binary codebooks, two sub-optimum searches are possible. 
     In the first sub-optimum search, each binary codebook is independently searched for a subset of optimum binary codevectors, say for example, the 5 best binary codevectors of each codebook of 16 codevectors is found, forming two optimum codevector subsets of 5 codevectors each. Then an exhaustive search of all combinations (25 in this example) of the optimum codevector subsets is performed. For the subset exhaustive search calculation, the filtering and auto-correlation terms from the first calculation of the optimum codevector subsets are available for reuse in the subsequent exhaustive search. In addition, the number of cross-correlation calculations, also 25, is substantially reduced compared to the number of cross-correlation calculations required in an exhaustive search of the full codebook sets, i.e. 256. 
     In a second sub-optimum search, the one best binary codevector is found from the set consisting of both the first and second binary codebooks. Then an exhaustive search is performed using the one best binary codevector in combination with each of the codevectors from the other binary codebook which did not contain the one best binary codevector. In the second sub-optimum search, the filtering and auto-correlation terms from the first calculation of the fidelity criterion function for the one best binary codevector are available for reuse in the subsequent exhaustive search of the other binary codebook. In addition, the number of cross-correlation calculations is further reduced to 16, which is less than the number of cross-correlation calculations required in an exhaustive search of the full codebook sets or using the optimum subsets. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a diagram of a CELP encoder utilizing a ternary codebook in accordance with the present invention. 
     FIG. 2 is a block diagram of a CELP decoder utilizing a ternary codebook in accordance with the present invention. 
     FIG. 3 is a flow diagram of an exhaustive search process for finding an optimum codevector in accordance with the present invention. 
     FIG. 4 is a flow diagram of a first sub-optimum search process for finding a codevector in accordance with the present invention. 
     FIG. 5 is a flow diagram of a second sub-optimum search process for finding a codevector in accordance with the present invention. 
     FIGS. 6A,  6 B, and  6 C is a graphical representations of a first binary codevector, a second binary codevector, and a ternary codevector, respectively. 
    
    
     DETAILED DESCRIPTION 
     CELP Encoding 
     The CELP encoder of FIG. 1 includes an input terminal  10  for receiving input speech samples which have been converted to digital form. The CELP encoder represents the input speech samples as digital parameters comprising an LSP index, a pitch lag and gain, and a code index and gain, for digital multiplexing by transmitter  30  on communication channel  31 . 
     LSP Index 
     As indicated above, speech signals are produced as a result of acoustical excitation of the vocal tract. The input speech samples received on terminal  10  are processed in accordance with known techniques of LPC analysis  26 , and are then quantized by a line spectral pair (LSP) quantization circuit  28  into a conventional LSP index. 
     Pitch Lag and Gain 
     Pitch lag and gain are derived from the input speech using a weighted synthesis filter  16 , and an adaptive codebook analysis  18 . The parameters of pitch lag and gain are made adaptive to the voice of the speaker, as is known in the art. The prediction error between the input speech samples at the output of the perceptual weighting filter  12 , and predicted reconstructed speech samples from a weighted synthesis filter  16  is available at the output of adder  14 . The perceptual weighting filter  12  attenuates those frequencies where the error is perceptually more important. The role of the weighting filter is to concentrate the coding noise in the formant regions where it is effectively masked by the speech signal. By doing so, the noise at other frequencies can be lowered to reduce the overall perceived noise. Weighted synthesis filter  16  represents the combined effect of the decoder synthesis filter and the perceptual weighting filter  12 . Also, in order to set the proper initial conditions at the subframe boundary, a zero input is provided to weighted synthesis filter  16 . The adaptive codebook analysis  18  performs predictive analysis by selecting a pitch lag and gain which minimizes the instantaneous energy of the mean squared prediction error. 
     Innovation Code Index and Gain 
     The innovation code index and gain is also made adaptive to the voice of the speaker using a second weighted synthesis filter  22 , and a ternary codebook analysis  24 , containing an encoder ternary codebook of the present invention. The prediction error between the input speech samples at the output of the adder  14 , and predicted reconstructed speech samples from a second weighted synthesis filter  22  is available at the output of adder  20 . Weighted synthesis filter  22  represents the combined effect of the decoder synthesis filter and the perceptual weighting filter  12 , and also subtracts the effect of adaptive pitch lag and gain introduced by weighted synthesis filter  16  to the output of adder  14 . 
     The ternary codebook analysis  18  performs predictive analysis by selecting an innovation sequence which maximizes a given fidelity criterion function. The ternary codebook structure is readily understood from a discussion of CELP decoding. 
     CELP Decoding 
     A CELP system decoder is shown in FIG. 2. A digital demultiplexer  32  is coupled to a communication channel  31 . The received innovation code index (index i and index j), and associated gain is input to ternary decoder codebook  34 . The ternary decoder codebook  34  is comprised of a first binary codebook  36 , and a second binary codebook  38 . The output of the first and second binary codebooks are added together in adder  40  to form a ternary codebook output, which is scaled by the received signed gain in multiplier  42 . In general, any two digital codebooks may be added to form a third digital codebook by combining respective codevectors, such as a summation operation. 
     To illustrate how a ternary codevector is formed from two binary codevectors, reference is made to FIGS. 6A,  6 B and  6 C. A first binary codevector is shown in FIG. 6A consisting of values {0,1}. A second binary codevector is shown in FIG. 6B consisting of values {−1,0}. By signed addition in adder  40  of FIG. 2, the two binary codevectors form a ternary codevector, as illustrated in FIG.  6 C. 
     The output of the ternary decoder codebook  34  in FIG. 2 is the desired innovation sequence or the excitation input to a CELP system. In particular, the innovation sequence from ternary decoder codebook  34  is combined in adder  44  with the output of the adaptive codebook  48  and applied to LPC synthesis filter  46 . The result at the output of LPC synthesis filter  46  is the reconstructed speech. As a specific example, if each speech frame is 4 milliseconds, and the sampling rate is 8 Mhz, then each innovation sequence, or codevector, is 32 samples long. 
     Optimum Innovation Sequence Selection 
     The ternary codebook analysis  24  of FIG. 1 is illustrated in further detail by the process flow diagram of FIG.  3 . In code excited linear prediction coding, the optimum codevector is found by maximizing the fidelity criterion function,                MAX   k                         (       x   t          Fc   k       )     2                      Fc   k                    2                 (     equation                 1     )                                
     where x t  is the target vector representing the input speech sample, F is an N×N matrix with the term in the n th row and the i th column given by f n−i , and C k  is the k th codevector in the innovation codebook. Also, ∥ ∥ 2  indicates the sum of the squares of the vector components, and is essentially a measure of signal energy content. The truncated impulse response f n , n=1,2. . . N, represents the combined effects of the decoder synthesis filter and the perceptual weighting filter. The computational burden of the CELP encoder comes from the evaluation of the filtered term Fc k  and the cross-correlation, auto-correlation terms in the fidelity criterion function. 
     
       
         Let  C   k =θ i = 72   j , 
       
     
     K=0, 1, . . . K−1 
     i=0, 1, . . . I−1 
     j=0, 1, . . . J−1 
     Log 2  K=Log 2  I+Log 2  J, where θ i , η j  are codevectors from the two binary codebooks, the fidelity criterion function for the codebook search becomes,                Ψ        (     i   ,   j     )       =         (         x   t        F                   θ   i       +       x   t        F                   η   j         )     2           θ   i   t          F   t        F                   θ   i       +     2        θ   i   t          F   t        F                   η   j       +       η   j   t          F   t        F                   η   j                   (     equation                 2     )                                
     Search Procedures 
     There are several ways in which the fidelity criterion function Ψ(i,j) may be evaluated. 
     1. Exhaustive Search. 
     Finding the maximum Ψ(i, j) involves the calculation of Fθ i , Fη j  and θ i   t F t Fη j , which has I and J filtering and the IJ cross-correlation of x t Fθ i , x t Fη j  and ∥Fθ i ∥ 2 , ∥Fη j ∥ 2 , which has I+J cross-correlation and I+J auto-correlation terms. 
     FIG. 3 illustrates an exhaustive search process for the optimum innovation sequence. All combinations of binary codevectors in binary codebooks  1  and  2  are computed for the fidelity criterion function ⊥T(i, j). The peak fidelity criterion function ΨT(i, j) is, selected at step  62 , thereby identifying the desired codebook index i and codebook index j. 
     Binary codebook  1  is selectively coupled to linear filter  50 . The output of linear filter  50  is coupled to correlation step  52 , which provides a correlation calculation with the target speech vector X, the input speech samples filtered in a perceptual weighting filter. Binary codebook  2  is selectively coupled to linear filter  68 . The output of linear filter  68  is coupled to correlation step  72 , which provides a correlation calculation with the target speech vector X. The output of correlation step  52  is coupled to one input of adder  66 . The output of correlation step  72  is coupled to the other input of adder  66 . The output of adder  66  is coupled to a square function  64  which squares the output of the adder  66  to form a value equal to the numerator of the fidelity criterion Ψ(i, j) of equation 2. The linear filters  50  and  68  are each equivalent to the weighted synthesis filter  22  of FIG.  1  and are used only in the process of selecting optimum synthesis parameters. The decoder (FIG. 2) will use the normal synthesis filer. 
     The output of linear filter  50  is also coupled to a sum of the squares calculation step  54 . The output of linear filter  68  is further coupled to a sum of the squares calculation step  70 . The sum of the squares is a measure of signal energy content. The linear filter  50  and the linear filter  68  are also input to correlation step  56  to form a cross-correlation term between codebook  1  and codebook  2 . The cross-correlation term output of correlation step  56  is multiplied by 2 in multiplier  58 . Adder  60  combines the output of multiplier  58 , the output of sum of the squares calculation step  54  plus the output of sum of the squares calculation step  70  to form a value equal to the demomimator of the fidelity criterion ΨT(i, j) of equation 2. 
     In operation, one of  16  codevectors of binary codebook  1  corresponding to a 4 bit codebook index i, and one of 16 codevectors of binary codebook  2  corresponding to a 4 bit codebook index j, is selected for evaluation in the fidelity criterion. The total number of searches is 16×16, or 256. Hoverer, the linear filtering steps  50 ,  68 , the auto-correlation calculations  52 ,  72  and the sum of the squares calculation  54 ,  70  need only be performed 32 times (not 256 times), or once for each of 16 binary codevectors in two codebooks. The results of prior calculations are saved and reused, thereby reducing the time required to perform an exhaustive search. The number of cross-correlation calculations in correlation step  56  is equal to 256, the number of binary vector combinations searched. 
     The peak selection step  62  receives the numerator of equation 2 on one input and the denominator of equation 2 on the other input for each of the 256 searched combinations. Accordingly, the codebook index i and codebook index j corresponding to a peak of the fidelity criterion function Ψ(i, j) is identified. The ability to search the ternary codebook  34 , which stores 256 ternary codevectors, by searching among only 32 binary codevectors, is based on the superposition property of linear filters. 
     2. Sub-Optimum Search I 
     FIG. 4 illustrates an alternative search process for the codebook index i and codebook index j corresponding to a desired codebook innovation sequence. This search involves the calculation of equation 1 for codebook  1  and codebook  2  individually as follows:                    (       x   t        F                   θ   i       )     2                    F                   θ   i                    2                       and                       (       x   t        F                   η   j       )     2                    F                   η   j                    2                 (     equation                 3     )                                
     To search all the codevectors in both codebooks individually, only 16 searches are needed, and no cross-correlation terms exist. A subset of codevectors (say 5) in each of the two binary codebooks are selected as the most likely candidates. The two subsets that maximizes the fidelity criterion functions above are then jointly searched to determine the optimum, as in the exhaustive search in FIG.  3 . Thus, for a subset of 5 codevectors in each codebook, only 25 joint searches are needed to exhaustively search all subset combinations. 
     In FIG. 4, binary codebook  1  is selectively coupled to linear filter  74 . The output of linear filter  74  is coupled to a squared correlation step  76 , which provides a squared correlation calculation with the target speech vector X. The output of linear filter  74  is also coupled to a sum of the squares calculation step  78 . The output of the squared correlation step  76 , and the sum of the squares calculation step  78  is input to peak selection step  80  to select a candidate subset of codebook  1  vectors. 
     Binary codebook  2  is selectively coupled to linear filter  84 . The output of linear filter  84  is coupled to a squared correlation step  86 , which provides a squared correlation calculation with the target speech vector X. The output of linear filter  84  is also coupled to a sum of the squares calculation step  88 . The output of the squared correlation step.  86 , and the sum of the squares calculation step  88  is input to peak selection step  80  to select a candidate subset of codebook  2  vectors. In such manner a fidelity criterion function expressed by equation 3 is carried out in the process of FIG.  4 . 
     After the candidate subsets are determined, an exhaustive search as illustrated in FIG. 3 is performed using the candidate subsets as the input codevectors. In the present example, 25 searches are needed for an exhaustive search of the candidate subsets, as compared to 256 searches for the full binary codebooks. In addition, filtering and auto-correlation terms from the first calculation of the optimum binary codevector subsets are available for reuse in the subsequent exhaustive search of the candidate subsets. 
     3. Sub-Optimum Search II 
     FIG. 5 illustrates yet another alternative search process for the codebook index i and codebook index j corresponding to a desired codebook innovation sequence. This search evaluates each of the binary codevectors individually in both codebooks using the same fidelity criterion function as given in equation 3 to find the one binary codevector having the maximum value of the fidelity criterion function. The maximum binary codevector, which may be found in either codebook (binary codebook  1  or binary codebook  2 ), is then exhaustively searched in combination with each binary codevector in the otter binary codebook (binary codebook  2  or binary codebook  1 ), to maximize the fidelity criterion function Ψ(i, j). 
     In FIG. 5, binary codebooks  1  and  2  are treated as a single set of binary codevectors, as schematically represented by a data bus  93  and selection switches  94  and  104 . 
     That is, each binary codevector of binary codebook  1  and binary codebook  2  is selectively coupled to linear filter  96 . The output of linear filter  96  is coupled to a squared correlation step  98 , which provides a squared correlation calculation with the target speech vector X. The output of linear filter  96  is also coupled to a sum of the squares calculation step  100 . The output of the squared correlation step  98 , and the sum of the squares calculation step  100  is input to peak selection step  102  to select a single optimum codevector from codebook  1  and codebook  2 . A total of 32 searches is required, and no cross-correlation terms are needed. 
     Having found the optimum binary codevector from codebook  1  and codebook  2 , an exhaustive search for the optimum combination of binary codevectors  106  (as illustrated in FIG. 3) is performed using the single optimum codevector found as one set of the input codevectors. In addition, instead of exhaustively searching both codebooks, switch  104  under the control of the peak selection step  102 , selects the codevectors from the binary codebook which does not contain the single optimum codevector found by peak selection step  102 . In other words, if binary codebook  2  contains the optimum binary codevector, then switch  104  selects the set of binary codevectors from binary codebook  1  for the exhaustive search  106 , and vice versa. In such manner, only 16 exhaustive searches need be performed. As before, filtering and auto-correlation terms from the first calculation of the optimum single optimum codevector from codebook  1  and codebook  2  are available for reuse in the subsequent exhaustive search step  106 . The output of search step is the codebook index i and codebook index j representing the ternary innovation sequence for the current frame of speech. 
     Overlapping Codebook Structures 
     For any of the foregoing search strategies, the calculation of Fθ i , Fη j  can be further accelerated by using an overlapping codebook structure as indicated in cited U.S. Pat. No. 4,797,925 to the present inventor. That is, the codebook structure has adjacent codevectors which have a subset of elements in common. An example of such structure is the following two codevectors: 
     
       
         θ L   t =( g   L   , g   L+1   , . . . , g   L+N−1 ) 
       
     
     
       
         θ L+1   t =( g   L+1   , g   L+2   , . . . , g   L+N)   
       
     
     Other overlapping structures in which the starting positions of the codevectors are shifted by more than one sample are also possible. With the overlapping structure, the filtering operation of Fθ i  and Fη j  can be accomplished by a procedure using recursive endpoint correction in which the filter response to each succeeding code sequence is then generated from the filter response to the preceding code sequence by subtracting the filter response to the first sample g L , and appending the filter response to the added sample g L+N . In such manner, except for the first codevector, the filter response to each successive codevector can be calculated using only one additional sample.

Technology Classification (CPC): 6