EP0734013B1 - Determination of an excitation vector in a CELP coder - Google Patents

Description

The present invention relates to the compression of speech signals to be transmitted over a telephone line and more particularly the determination of an excitation vector as part of a compression using the Prediction method Linear Excited by Code (CELP).

Figure 1 very schematically represents a circuit CELP compression. Such a circuit is based on modeling vocal cords and the built-up resonance chamber through the oral cavity, throat and larynx. Such a compression method is therefore optimized for the treatment of speech signals.

The oral, throat and larynx cavities are modeled by a "linear prediction" filter 10 whose transfer function generally has ten poles. The ropes are modeled by an excitation E treated by a comb filter 12.

A digitized speech signal S is analyzed in frames by an analysis circuit 14. At each frame, the analysis circuit 14 determines coefficients a ₁ to a ₁₀ of the transfer function of the filter 10, the step p of the filter in comb 12, and a gain G applied to the excitation E at 16 at the input of the filter 12. The values a _i , p and G are calculated for each frame to take account respectively of the variations of the oral cavity, of the spectrum frequency of the vocal cords and the amplitude of the sound. In this way an attempt is made to obtain that the output of the filter 10 is equal to the signal S. Then, instead of transmitting the samples of the signal S, the coefficients a _i , p, and G are transmitted so that a decoder which receives these coefficients reconstruct the corresponding frames of the signal S.

Of course, the decoder must also know the excitation E to use. The determination of the coefficients ai, p and G does not pose any particular problem. However, the procedure finding the optimal excitement remains the heaviest in terms of computational load and it's still a big interest to simplify it even at the cost of a significant reduction the quality of the compression.

At the origin of the CELP coding, the excitation E was selected in a table 18 (called "codebook") containing several possible excitations which in fact represented pieces of white noise. In this case, a control circuit 20 scan table 18 until the difference e, formed at 22, between the current frame of signal S and the corresponding frame at the output of the filter 10 is minimal. (Of course, instead of compare the signal S to the output of the filter 10, we can also compare the excitation E to the frame of the signal S having undergone the reverse processing of filters 10 and 12.)

With this technique, in addition to the coefficients ai, p and G, we provide the address C selecting the best excitation E in table 18 to a decoder provided with a peer table.

Each excitation contained in table 18 is a sequence of corresponding digital samples respectively to the samples of each of the frames of the signal to be compressed. For compression to be of acceptable quality, it is necessary to store a relatively large number of around 1000, excitation sequences.

To limit the complexity of the search procedure, it has been proposed that each sample of an excitation sequence can only take three values, namely, 0, 1 or -1 (ternary excitation sequence). We realized that not noticeably change the quality of the compression.

Figure 2 shows an example of excitation sequence E which has been proposed to further reduce the complexity of research. This excitation sequence is called binary. It includes several non-zero samples with values 1 and -1, two non-zero samples, or pulses, being separated by a constant number of null samples, here 3. Such a sequence excitation can be represented by a binary number (or excitation code) C whose bits are associated with the pulses and correspond to the polarity of these. By proceeding thus, the code C supplied by the control circuit 20 corresponds directly to an excitation sequence and table 18 is deleted. The complexity is also reduced by the fact that the samples to be taken into account are reduced to pulses, whose number is, in the example of figure 2, four times less than the total number of samples in a sequence. In in addition, the structure of filters 10 and 12 is simplified.

This technique slightly degrades the quality of compression, but this degradation is compensated so simple treatment to suppress the effects of regularity of spacing of non-zero samples.

Each C code is associated with an excitation vector C having as components the values 1 and -1 corresponding to the bits at 0 and 1 of code C. The terms "vector" and "code" are confused in the following description.

To further reduce the number of trials for minimize the error, we proposed to limit the number of vectors or possible excitation codes to a representative subset of a larger whole. The article entitled "A Comparison of some Algebraic Structures for CELP Coding of Speech "by J.P. Adoul and C. Lamblin in Proc. ICASSP, 1987, describes such a method. To create a representative subset of all C codes of N bits, we form the set of values of n bits (n <N), each of which is supplemented by N-n bits error correction.

To find the best excitation vector C, we generally seek to maximize a selection criterion defined by: m = scal 2 (T, C i ) / mod 2 (FC i ) where C _i is the excitation vector tested; where T is a target vector formed by samples of the frame being analyzed of the signal S having undergone the reverse processing of filters 10 and 12, these samples being those corresponding to the values 1 and -1 of the vector C _i ; and where F denotes the matrix representing the transfer function of filters 10 and 12, in which only the rows corresponding to the values 1 and -1 of the vector C _i have been kept. The notations scal (.,.) And mod (.,.) Denote the scalar product and the module respectively.

The test of all the excitation vectors C _i according to this criterion represents a considerable number of calculations to be carried out between the arrivals of two frames of the signal S.

It was found that the denominator of the criterion m is approximately constant, whatever the excitation vector C _i . Thus, the criterion m is approximately maximized by maximizing the numerator. This numerator is maximized when each component of the excitation vector C _i is that of the same sign as the corresponding sample of the target vector T. In other words, an approximate optimal excitation code is obtained directly by taking for its bits the sign bits of the target vector samples (or the inverse of the sign bits).

This solution is not applicable in the case where the usable excitation codes are limited to a subset representative of a larger set obtained, for example, at using an error correction code.

An object of the present invention is to provide a method for limiting the number of calculations required to maximize the aforementioned criterion m in the case where the usable excitation codes belong to a subset representative of a larger whole.

To achieve this object, the present invention provides a method for determining an excitation vector associated with a frame of a speech signal to be compressed according to claim 1.

According to an embodiment of the present invention, the error correction bits are bits of a correction code from Hamming.

la figure 1, précédemment décrite, illustre une méthode de compression CELP ;

la figure 2, précédemment décrite, représente un exemple de séquence d'excitation et du code correspondant ; et

la figure 3 illustre des traitements à effectuer selon la présente invention pour sélectionner un vecteur d'excitation optimal dans le cas où celui-ci appartient à un sous-ensemble obtenu en utilisant un code correcteur d'erreurs.

These objects, characteristics and advantages as well as others of the present invention will be explained in detail in the following description of particular embodiments, given without limitation by means of the attached figures, among which:

Figure 1, previously described, illustrates a CELP compression method;

FIG. 2, previously described, shows an example of excitation sequence and the corresponding code; and

FIG. 3 illustrates the processing operations to be carried out according to the present invention for selecting an optimal excitation vector in the case where it belongs to a subset obtained using an error correcting code.

To maximize the above-mentioned criterion m, it has been found that the denominator of this criterion, the square of the module of the vector FC _i , is approximately constant, whatever the excitation vector C _i . This approximation is relatively good since the modulus of the vector C _i is constant. Thus, in order to maximize the criterion m, it suffices to maximize a simplified criterion which is the scalar product of the target vector T by the excitation vector C _i . This scalar product is maximum when each component (1 or -1) of the vector C _i is that of the same sign as the corresponding sample of the target vector T. We thus obtain directly an optimal excitation vector approximated Copt from the target vector T.

This process is not directly applicable in the cases where the possible excitation codes belong to a subset representative of a larger whole, for example when this subset is formed from values of n bits to which we add N-n bits of an error correcting code. In effect then it is very likely that the excitation code found does not belong to the subset. In this case, we might consider reducing the excitation code found to a excitation code belonging to the subset by applying it an error correction function associated with the correction code. We then fall on the excitation code of the subset which is closest to the excitation code found. This "error correction" has the effect of modifying at least one bit of the excitation code, this bit can have in some cases a great influence on the value of the criterion m, so that the final excitation code gives poor results.

By way of example, a Hamming correcting code denoted H (N, n, 3) is used below, where 3 denotes the minimum Hamming distance separating two elements belonging to the representative subset. The Hamming distance between two values is defined as the number of bitwise differences between these two values. With this solution, we create a subset of 2 ⁿ excitation vectors of N bits.

According to the invention, a group of excitation codes is formed including an initial code found by maximizing the simplified criterion m as well as all the other codes obtained at from there by changing a single bit. This has the consequence, using a Hamming correcting code to correct a single bit (minimum Hamming distance 3), that each of the group's excitation codes is close to a code distinct from the usable subset. Then we apply to each of the group codes the error correction function of Hamming, which brings each of the group's codes back to the code closer to the subset. We get a group of codes "corrected" belonging to the subset and which "surrounds" the code originally found. Among the corrected codes, we retain as approximate optimal code that which maximizes the complete criterion m, that is, by calculating its numerator and denominator.

FIG. 3 represents in diagram form the method according to the invention which has just been described. The analyzed frame of the signal S undergoes the reverse processing at 24 of that of the filters 10 and 12 of FIG. 1. A target vector T is thus obtained, of which only the samples corresponding to the pulses of the excitation sequences are kept. In 26, samples of the vector T are retained only the sign bits (or their inverses) to provide an initial excitation code C ₀ . This code C ₀ is "corrupted" at 28 to form a group of codes comprising the code C ₀ and all the other codes C ₁ to C _N obtained by modifying a single bit of the code C ₀ . Each code C ₀ to C _N undergoes an "error correction" at 30 to provide a group of corrected codes C ' ₀ to C' _N. In 32, each of the vectors associated with the corrected codes is compared with the target vector T, and the optimal associated excitation code Copt is that associated with the vector which maximizes the complete criterion m.

Generally, for best results, the position of the first pulse of the excitation sequences E is variable. In the example in Figure 2, this position can be one of the first four, which is determined by two additional bits to be transmitted to the decoder and which multiplies by four the number of excitation vectors to be tested. In this case, we start by forming, for each of the four positions possible, a target vector and an excitation vector whose components are those of the same sign as those of the vector corresponding target. We use as excitation vector optimal approached, among the four vectors thus obtained, that which maximizes the complete criterion m.

Claims (2)

1. A method for determining an excitation vector (Copt) associated with a frame of a speech signal (S) to compress, said vector belonging to a subset associated with a larger set of excitation vectors likely to maximize a criterion (m), and having as components values 1 and -1 corresponding to a sequence of excitation samples (E) of a linear prediction filter (10), said criterion being equal to the square of the ratio between, on the one hand, the scalar product of the excitation vector by a target vector (T) formed by samples of the frame submitted to an inverse linear prediction filtering and, on the other hand, the module of the excitation vector submitted to a direct linear prediction filtering, the excitation vectors being associated with excitation codes having bits corresponding to the signs of the components of the excitation vectors, the excitation code subset associated with said vector subset being formed by binary values completed by error correction bits, any excitation code being associated with an excitation code of the subset through an error correction function, characterized in that it includes the following steps:

   preselecting an excitation vector (C₀) having as components those with the same signs as the corresponding samples of the target vector, or those with the opposite signs; and

   if the preselected excitation vector (C₀) does not belong to said subset, selecting as an excitation vector the vector which maximizes said criterion (m) among the vectors (C'₀... C'_N) of the subset, which are respectively obtained by submitting the error correction function the preselected associated with the preselected vector (C_o) and the group of codes (C₁... C_N) the closest therefrom, formed so that each of the closest codes differs from the preselected code by a single bit; and

   selecting as the excitation code (Copt), among the corrected codes, the one associated with the vector which maximizes said criterion (m).
2. A method according to claim 1, wherein the error correction bits are the bits of a Hamming correcting code.

Images