Classifications

• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
  • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
  • G10L19/08—Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters
  • G10L19/10—Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters the excitation function being a multipulse excitation

Definitions

• the present invention relates to speech coding using synthetic analysis.
• a linear prediction of the speech signal is carried out to obtain the coefficients of a short-term synthesis filter modeling the transfer function of the vocal tract. These coefficients are transmitted to the decoder, as well as parameters characterizing an excitation to be applied to the short-term synthesis filter.
• further research is carried out on the longer-term correlations of the speech signal in order to characterize a long-term synthesis filter accounting for the pitch of the speech.
• the excitation indeed has a predictable component which can be represented by the past excitation, delayed by TP samples of the speech signal and affected by a gain g p .
• the remaining, unpredictable part of the excitation is called stochastic excitation.
• CELP Code Excited Linear Prediction
• MPLPC Multi-Pulse Linear Prediction Coding
• the stochastic excitation comprises a certain number of pulses whose positions are sought by the coder.
• CELP coders are preferred for low transmission rates, but they are more complex to implement than MPLPC coders.
• An object of the present invention is to obtain a good compromise between the quality of coding and the complexity of looking for stochastic excitement while getting a good robustness to transmission errors.
• the invention thus proposes a coding method using analysis by synthesis of a speech signal digitized in frames successive divided into sub-frames of lst samples, in which is analyzed for each frame by linear prediction to determine the coefficients of a short-term synthesis filter, and we determine for each subframe an excitation sequence which, subject to the filter short-term synthesis, produces a synthetic signal representative of the speech signal.
• the excitation sequence involves a stochastic excitation constituted by several pulses whose respective positions are calculated in the subframe and associated gains respectively. We subdivide each subframe in ns segments, ns being at least a number equal to the number np of pulses by stochastic excitation.
• the positions of the stochastic excitation pulses relative to a subframe are successively determined: we are looking for the first pulse at any position of the subframe, and the following positions excluding each segment to which an impulse belongs, the position was previously determined. Order numbers segments occupied by an excitation pulse stochastic are quantified separately from positions relative pulses in the occupied segments.
• segmental search for impulses the advantage of allowing good robustness to errors of transmission thanks in particular to the fact that we quantify in a way separate the sequence numbers of the segments occupied by a pulse of stochastic excitation and positions relative pulses in the occupied segments.
• This mode of seeking stochastic excitation is can be used advantageously even when not not implementing the segment research described above, and is also not applicable exclusively to coders MPLPC. It is particularly applicable to so-called VSELP coders where contributions to stochastic excitation are vectors chosen from a predetermined dictionary (see I. Gerson and M. Jasiuk: "Vector Sum Excited Linear Prediction (VSELP) Speech Coding at 8 kb / s ", Proc. Int. Conf. On Acoustics, Speech and Signal Processing, Albuquerque 1990, Vol. 1, pages 461-464).
• a speech coder implementing the invention is applicable in various types of speech transmission and / or storage systems using a digital compression technique.
• the speech coder 16 is part of a mobile radio station.
• the speech signal S is a digital signal sampled at a frequency typically equal to 8 kHz.
• the signal S comes from an analog-digital converter 18 receiving the amplified and filtered output signal from a microphone 20.
• the converter 18 puts the speech signal S in the form of successive frames themselves subdivided into nst sub-frames lst samples.
• the speech signal S can also be subjected to conventional shaping treatments such as Hamming filtering.
• the speech coder 16 delivers a binary sequence with a significantly lower bit rate than that of the speech signal S, and addresses this sequence to a channel coder 22 whose function is to introduce redundancy bits into the signal in order to allow detection and / or a correction of any transmission errors.
• the output signal from the channel encoder 22 is then modulated on a carrier frequency by the modulator 24, and the modulated signal is transmitted on the air interface.
• the speech coder 16 is a coder with analysis by synthesis.
• the encoder 16 determines on the one hand parameters characterizing a short-term synthesis filter modeling the vocal tract of the speaker, and on the other hand a sequence excitation which, applied to the short synthesis filter term, provides a synthetic signal constituting a estimation of the speech signal S according to a criterion of perceptual weighting.
• the coefficients a i are determined by a module 26 for short-term linear prediction analysis of the speech signal S.
• the a i are the linear prediction coefficients of the speech signal S.
• the order q of the linear prediction is typically of the order of 10.
• the methods applicable by module 26 for short-term linear prediction are well known in the field of speech coding.
• Module 26, for example, implements the Durbin-Levinson algorithm (see J. Makhoul: "Linear Prediction: A tutorial review", Proc. IEEE, Vol.63, N ° 4, April 1975, p. 561-580 ).
• the coefficients a i obtained are supplied to a module 28 which converts them into spectral line parameters (LSP).
• the LSP parameters can be obtained by the conversion module 28 by the classical method of Chebyshev polynomials (see P. Kabal and RP Ramachandran: "The computation of line spectral frequencies using Chebyshev polynomials", IEEE Trans. ASSP, Vol.34, N ° 6, 1986, pages 1419-1426). These are quantization values of the LSP parameters, obtained by a quantization module 30, which are transmitted to the decoder so that the latter finds the coefficients a i of the short-term synthesis filter.
• the non-quantified LSP parameters are supplied by the module 28 to a module 32 for calculating the coefficients of a perceptual weighting filter 34.
• the coefficients of the perceptual weighting filter are calculated by the module 32 for each subframe after interpolation of the LSP parameters received from the module 28.
• the perceptual weighting filter 34 receives the speech signal S and delivers a perceptually weighted SW signal which is analyzed by modules 36, 38, 40 for determine the excitation sequence.
• the excitation sequence of the short-term filter consists of an excitation predictable by a long-term synthetic filter modeling the pitch of the speech, and an excitement unpredictable stochastic, or innovation sequence.
• Module 36 performs long-term prediction (LTP) in open loop, i.e. it does not contribute directly to the minimization of the weighted error.
• LTP long-term prediction
• the weighting filter 34 intervenes in upstream of the open loop analysis module, but it could otherwise: module 36 could operate directly on the speech signal S or on the signal S cleared of its short-term correlations by a filter transfer function A (z).
• modules 38 and 40 operate in a closed loop, i.e. they directly contribute to minimizing the error perceptually weighted.
• Long-term prediction lag is determined in two steps.
• the analysis module 36 Open loop LTP detects voiced frames from the speech signal and determines, for each voiced frame, a degree of voicing MV and a delay search interval long-term prediction.
• the search interval is defined by a central value represented by its quantification index ZP and by a width in the field of quantification indexes, depending on the degree of voicing MV.
• the module 30 operates the quantization of the LSP parameters which have previously been determined for this frame.
• This quantization is for example vectorial, that is to say it consists in selecting, from one or more predetermined quantization tables, a set of quantized parameters LSP Q which has a minimum distance from the set of parameters LSP provided by the module 28.
• the quantification tables differ according to the degree of voicing MV provided to the quantization module 30 by the open-loop analyzer 36.
• a set of quantization tables for a degree of voicing MV is determined, during prior tests, so as to be statistically representative of frames having this degree MV. These sets are stored both in the coders and in the decoders implementing the invention.
• the module 30 delivers the set of quantized parameters LSP Q as well as its index Q in the applicable quantification tables.
• the speech coder 16 further comprises a module 42 for calculating the impulse response of the compound filter short-term synthesis filter and perceptual weighting.
• This compound filter has the function transfer W (z) / A (z).
• the module 42 takes for the filter of perceptual weighting W (z) that corresponding to LSP parameters interpolated but not quantified, i.e. the one whose coefficients were calculated by the module 32, and for the synthesis filter 1 / A (z) the corresponding one quantized and interpolated LSP parameters, i.e. the one that will be effectively reconstructed by the decoder.
• the TP delay index is ZP + DP.
• the gain g p is calculated by the stochastic analysis module 40.
• the stochastic excitation determined for each subframe by the module 40 is of the multi-pulse type.
• the positions and gains calculated by the analysis module 40 stochastics are quantified by a module 44.
• a module 48 is thus provided in the encoder which receives the different parameters and which adds to some of them redundancy bits to detect and / or correct any transmission errors.
• the degree of voicing MV coded on two bits being a critical parameter, we want it to reach the decoder with as few errors as possible. For this reason, redundancy bits are added to this parameter by module 48.
• the channel encoder 22 is that used in the pan-European radiocommunication system with mobiles (GSM).
• GSM pan-European radiocommunication system with mobiles
• This channel encoder described in detail in GSM Recommendation 05.03, has been finalized for an RPE-LTP type 13 kbit / s speech coder which also produces 260 bits per 20 ms frame. The sensibility of each of the 260 bits was determined from tests listening.
• the bits from the source encoder have been grouped into three categories. The first of these AI categories groups 50 bits which are convolutionally coded on the base of a generator polynomial giving a redundancy of a half with a constraint length equal to 5. Three bits parity are calculated and added to the 50 bits of the category AI before convolutional coding.
• the second category (IB) has 132 bits which are protected at a rate of half a by the same polynomial as the previous category.
• the third category (II) contains 78 unprotected bits. After application of convolutional code, bits (456 per frame) are subject to interlacing.
• the scheduling module 46 of the new source coder implementing the invention distributes the bits in the three categories according to the subjective importance of these bits.
• a mobile radio station capable of receiving the speech signal processed by the source encoder 16 is shown schematically in Figure 2.
• the radio signal received is first processed by a demodulator 50 then by a channel 52 decoder which performs dual operations of those of modulator 24 and channel encoder 22.
• the decoder channel 52 provides the speech decoder 54 with a sequence binary which, in the absence of transmission errors or when any errors have been corrected by the decoder channel 52, corresponds to the binary sequence delivered by the scheduling module 46 at the coder 16.
• the decoder 54 includes a module 56 which receives this sequence binary and which identifies the parameters relating to the different frames and subframes.
• the module 56 also performs some checks on the parameters received. In particular, module 56 examines the redundancy bits introduced by the encoder module 48, to detect and / or correct the errors affecting the parameters associated with these bits of redundancy.
• a module 58 of the decoder receives the degree of voicing MV and the index of Q for quantizing the LSP parameters.
• the module 58 finds the quantized LSP parameters in the tables corresponding to the value of MV, and, after interpolation, converts them into coefficients a i for the short-term synthesis filter 60.
• a pulse generator 62 receives the positions p (n) of the np pulses of the stochastic excitation.
• the generator 62 delivers pulses of unit amplitude which are each multiplied by 64 by the associated gain g (n).
• the output of amplifier 64 is addressed to the long-term synthesis filter 66.
• This filter 66 has an adaptive directory structure.
• the output samples u of the filter 66 are stored in the adaptive directory 68 so as to be available for the subsequent subframes.
• the delay TP relative to a sub-frame, calculated from the quantization indices ZP and DP, is supplied to the adaptive repertoire 68 to produce the signal u suitably delayed.
• the amplifier 70 multiplies the signal thus delayed by the gain g p of long-term prediction.
• the long-term filter 66 finally comprises an adder 72 which adds the outputs of amplifiers 64 and 70 to provide the excitation sequence u.
• the excitation sequence is addressed to the short-term synthesis filter 60, and the resulting signal can also, in known manner, be subjected to a post-filter 74 whose coefficients depend on the synthesis parameters received, to form the signal of synthetic speech S '.
• the output signal S 'of the decoder 54 is then converted into analog by the converter 76 before being amplified to control a loudspeaker 78.
• the module 36 determines, for each sub-frame st, the entire delay K st which maximizes the open-loop estimation P st (k) of the long-term prediction gain on the sub-frame st, excluding the delays k for which the autocorrelation C st (k) is negative or smaller than a small fraction ⁇ of the energy R0 st of the subframe.
• step 94 the degree of voicing MV of the current frame is taken equal to 0 in step 94, which in this case ends the operations performed by the module 36 on this frame. If on the contrary the threshold S0 is exceeded in step 92, the current frame is detected as voiced and the degree MV will be equal to 1, 2 or 3. The module 36 then calculates, for each subframe st, a list I st containing candidate delays to constitute the ZP center of the search interval for long-term prediction delays.
• the module 36 determines the basic delay rbf in full resolution for the rest of the processing. This basic delay could be taken equal to the integer K st obtained in step 90. The fact of finding the basic delay in fractional resolution around K st however makes it possible to gain in precision.
• Step 100 thus consists in finding, around the integer delay K st obtained in step 90, the fractional delay which maximizes the expression C st 2 / G st .
• This search can be carried out at the maximum resolution of the fractional delays (1/6 in the example described here) even if the entire delay K st is not in the domain where this maximum resolution applies.
• the autocorrelations C st (T) and the delayed energies G st (T) are obtained by interpolation from the values stored in step 90 for the whole delays.
• the basic delay relating to a sub-frame could also be determined in fractional resolution from step 90 and taken into account in the first estimation of the overall prediction gain on the frame.
• step 102 the address j in the list I st and the index m of the submultiple are initialized to 0 and 1, respectively.
• a comparison 104 is made between the submultiple rbf / m and the minimum delay rmin. The submultiple rbf / m is to be examined if it is greater than rmin.
• step 110 If P st (r i ) ⁇ SE st , the delay r i is not taken into account, and we go directly to step 110 of incrementing the index m before carrying out the comparison 104 again for the next submultiple. If test 108 shows that P st (r i ) ⁇ SE st , the delay r i is retained and step 112 is executed before incrementing the index m in step 110. In step 112, we stores the index i at the address j in the list I st , we give the value m to the integer m0 intended to be equal to the index of the smallest submultiple retained, then we increment by one unit l 'address j.
• the examination of the sub-multiples of the basic delay is finished when the comparison 104 shows rbf / m ⁇ rmin.
• We then examine the multiple delays of the smallest rbf / m0 of the submultiples previously selected according to the process illustrated in FIG. 5. This examination begins with an initialization 114 of the index n of the multiple: n 2.
• a comparison 116 is made between the multiple n.rbf / m0 and the maximum delay rmax. If n.rbf / m0> rmax, test 118 is carried out to determine whether the index m0 of the smallest sub-multiple is an integer multiple of n.
• step 120 the delay n.rbf / m0 has already been examined when examining the sub-multiples of rbf, and we go directly to step 120 of incrementing the index n before carrying out again comparison 116 for the next multiple. If test 118 shows that m0 is not an integer multiple of n, the multiple n.rbf / m0 is to be examined. We then take for the integer i the value of the index of the quantized delay r i closest to n.rbf / m0 (step 122), then we compare, at 124, the estimated value of the prediction gain P st ( r i ) at the selection threshold SE st .
• step 120 If P st (r i ) ⁇ SE st , the delay r i is not taken into account, and we go directly to step 120 of incrementing the index n. If the test 124 shows that P st (r i ) ⁇ SE st , the delay r i is retained and step 126 is executed before incrementing the index n in step 120. In step 126, we stores the index i at address j in the list I st , then the address j is incremented by one.
• the list I st contains j candidate delay index. If we wish to limit the maximum length of the list I st to jmax for the following steps, we can take the length j st of this list equal to min (j, jmax) (step 128) and then, in step 130, order the list I st in the order of gains C st 2 (r Ist (j) ) / G st 2 (r Ist (j) ) decreasing for 0 ⁇ j ⁇ j st so as to keep only the j st delays providing the largest gain values.
• the value of jmax is chosen according to the compromise sought between the efficiency of the search for LTP delays and the complexity of this search. Typical values of jmax range from 3 to 5.
• the analysis module 36 calculates a quantity Ymax determining a second open-loop estimate of the prediction gain at long term over the entire frame, as well as indexes ZP, ZP0 and ZP1 in a phase 132, the progress of which is detailed in FIG. 6.
• This phase 132 consists in testing search intervals of length N1 to determine which one maximizes a second estimate of the overall prediction gain on the frame. The intervals tested are those whose centers are the candidate delays contained in the list I st calculated during phase 101.
• Phase 132 begins with a step 136 where the address j in the list I st is initialized to 0.
• step 138 we check if the index I st (j) has already been encountered by testing a previous interval centered on I st' (j ') with st' ⁇ st and 0 ⁇ j ' ⁇ j st' , in order to d '' Avoid testing the same interval twice. If test 138 reveals that I st (j) already appeared in a list I st ' with st' ⁇ st, we directly increment the address j in step 140, then we compare it to the length j st of the list I st . If the comparison 142 shows that j ⁇ j st , we return to step 138 for the new value of the address j.
• This value Ymax is for example initialized to 0 at the same time as the index st in step 96. If Y ⁇ Ymax, we go directly to step 140 for incrementing the index j. If the comparison 150 shows that Y> Ymax, step 152 is executed before incrementing the address j in step 140. At this step 152, the index ZP is taken equal to I st (j) and the indices ZP0 and ZP1 are respectively taken equal to the smallest and the largest of the indices i st ' determined in step 148.
• the index st is incremented by one (step 154) then compared, in step 156, to the number nst of subframes per frame. If st ⁇ nst, we return to step 98 to perform the operations relating to the following sub-frame.
• the index ZP denotes the center of the search interval that will be provided to the module 38 closed loop LTP analysis
• ZP0 and ZP1 are index whose difference is representative of the dispersion of optimal delays per subframe in the interval centered on ZP.
• Gp 20.log 10 (RO / RO-Ymax) .
• Two other thresholds S1 and S2 are used. If Gp ⁇ S1, the degree of voicing MV is taken equal to 1 for the current frame.
• Gp> S2 the dispersion of the optimal delays for the different sub-frames of the current frame is examined. If ZP1-ZP ⁇ N3 / 2 and ZP-ZP0 ⁇ N3 / 2, an interval of length N3 centered on ZP is sufficient to take into account all the optimal delays and the degree of voicing is taken equal to 3 (if Gp> S2) . Otherwise, if ZP1-ZP ⁇ N3 / 2 or ZP-ZPO> N3 / 2, the degree of voicing is taken equal to 2 (if Gp> S2).
• ZP + DP index of TP delay ultimately determined may therefore in some cases be more small than 0 or larger than 255. This allows analysis LTP in closed mouth to also carry on some delays TP smaller than rmin or larger than rmax. We improve thus the subjective quality of the so-called voice reproduction pathological and non-vocal signals (vocal frequencies DTMF or signaling frequencies used by the network dial-up).
• Reducing the delay search interval for very closely spaced frames reduces the complexity of the closed loop LTP analysis performed by the module 38 by reducing the number of convolutions y T (i) to be calculated according to formula (1).
• Another possibility is to provide a parity bit for the delay TP and / or the gain g p , making it possible to detect possible errors affecting these parameters.
• the first optimizations carried out in step 90 relative to the different sub-frames are replaced by a single optimization relating to the entire frame.
• We then determine the basic delay in whole resolution K which maximizes X (k) C 2 (k) / G (k) for rmin ⁇ k ⁇ rmax.
• P (K) 20.log 10 [R0 / [R0-X (K)]] .
• rbf a single basic delay in fractional resolution rbf and the examination 101 of the sub-multiples and of the multiples is carried out only once and produces a single list I instead of nst lists I st .
• Phase 132 is then performed only once for this list I, distinguishing the subframes only in steps 148, 150 and 152.
• This variant embodiment has the advantage of reducing the complexity of the analysis in open loop.
• nz basic delays K 1 ' , ..., K nz ' in full resolution.
• the voiced / unvoiced decision (step 92) is taken on the basis of that of the basic delays K i ' which provides the greatest value for the first open-loop estimate of the long-term prediction gain.
• the basic delays in fractional resolution are determined by the same process as in step 100, but only allowing the quantized delay values. Examination 101 of the submultiples and multiples is not carried out. For the phase 132 of calculating the second estimate of the prediction gain, the nz basic delays previously determined are taken as candidate delays. This second variant makes it possible to dispense with the systematic examination of the submultiples and of the multiples which are generally taken into account by virtue of the subdivision of the domain of possible delays.
• phase 132 is modified in that, in the optimization steps 148, the index i st ' which maximizes C st' 2 (r i ) / G st ' (r i ) for I st (j) -N1 / 2 ⁇ i ⁇ I st (j) + N1 / 2 and 0 ⁇ i ⁇ N, and on the other hand, during the same maximization loop, the index k st ' which maximizes this same quantity over a reduced interval I st (j) -N3 / 2 ⁇ i ⁇ I st (j) + N3 / 2 and 0 ⁇ i ⁇ N.
• Gp ' 20.log 10 [R0 / (R0-Ymax ')] .
• the sub-frames for which the prediction gain is negative or negligible can be identified by consulting the nst pointers. If necessary, the module 38 is deactivated for the corresponding sub-frames. This does not affect the quality of the LTP analysis since the prediction gain corresponding to these subframes will be almost zero anyway.
• Another aspect of the invention relates to the module 42 for calculating the impulse response of the weighted synthesis filter.
• the closed loop LTP analysis module 38 needs this impulse response h over the duration of a subframe to calculate the convolutions y T (i) according to formula (1).
• the stochastic analysis module 40 also needs it to calculate convolutions as will be seen below.
• the operations performed by the module 42 are for example in accordance with the flowchart of FIG. 7.
• the coefficients a k are those involved in the perceptual weighting filter, i.e. the linear prediction coefficients interpolated but not quantified, while in expression (3), the coefficients a k are those applied to the synthesis filter, i.e. the quantized and interpolated linear prediction coefficients.
• the module 42 determines the smallest length L ⁇ such that the energy Eh (L ⁇ -1) of the response impulse truncated at L ⁇ samples or at least equal to a proportion ⁇ of its total energy Eh (pst-1) estimated on pst samples.
• a typical value of ⁇ is 98%.
• the number L ⁇ is initialized to pst in step 162 and decremented by one as 166 as Eh (L ⁇ -2)> ⁇ .Eh (pst-1) (test 164).
• the length L ⁇ sought is obtained when the test 164 shows that Eh (L ⁇ -2) ⁇ ⁇ .Eh (pst-1).
• a correcting term ⁇ (MV) is added to the value of L ⁇ which has been obtained (step 168).
• This corrective term is preferably an increasing function of the degree of voicing.
• ⁇ (0) - 5
• ⁇ (3) + 7.
• the truncation length Lh of the impulse response is taken equal to L ⁇ if L ⁇ ⁇ nst and to nst otherwise.
• a third aspect of the invention relates to the module 40 of stochastic analysis used to model the unpredictable part of the excitement.
• the stochastic excitation considered here is of the multi-pulse type.
• the stochastic excitation relating to a subframe is represented by np pulses of positions p (n) and of amplitudes, or gains, g (n) (1 ⁇ n ⁇ np).
• the gain g p of long-term prediction can also be calculated during the same process.
• the excitation sequence relating to a sub-frame comprises nc contributions associated respectively with nc gains.
• the contributions are lst sample vectors which, weighted by the associated and summed gains correspond to the excitation sequence of the short-term synthesis filter.
• np vectors comprising only 0 except an impulse of amplitude 1.
• the vectors F p (n) are simply constituted by the vector of the impulse response h shifted by p (n) samples. Truncating the impulse response as described above therefore makes it possible to significantly reduce the number of operations useful for calculating the scalar products involving these vectors F p (n) .
• the gains g nc-1 (i) are the selected gains and the minimized quadratic error E is equal to the energy of the target vector e nc-1 .
• the decomposition of Cholesky and the inversion of the matrix M n however require to carry out divisions and calculations of square roots which are operations demanding in terms of computation complexity.
• the matrices L not R not .K not , R n , K n and L n -1 are each constructed by simply adding a line to the corresponding matrices of the previous iteration:
• the module 40 proceeds to the calculation 184 of the line n of the matrices L, R and K involved in the decomposition of the matrix B, which makes it possible to complete the matrices L n , R n and K n defined above.
• R (( not , k ) L (n, n) 1
• the column index j is first initialized at 0, in step 186.
• the integer k is also initialized to 0.
• a comparison 190 is then made between the integers k and j. If k ⁇ j, we add the term L (n, k). R (j, k) to the variable tmp, then we increment the whole k by one unit (step 192) before re-performing the comparison 190.
• a comparison 194 is made between the integers j and n. If j ⁇ n, the component R (n, j) is taken equal to tmp and the component L (n, j) to tmp.K (j) in step 196, then the column index j is incremented d 'a unit before returning to step 188 to calculate the following components.
• the inversion 200 then begins with an initialization 202 of the column index j 'at n-1.
• step 204 the term Linv (j ') is initialized to -L (n, j') and the integer k 'to j' + 1.
• a comparison 206 is then carried out between the integers k ′ and n. If k ' ⁇ n, we subtract the term L (k', j '). Linv (k') to Linv (j '), then we increment the whole k' by one unit (step 208) before re-executing comparison 206.
• the inversion 200 is followed by the calculation 214 of the reoptimized gains and of the target vector E for the following iteration.
• the computation of the reoptimized gains is also very simplified by the decomposition retained for the matrix B.
• K (( not ) and g not (i ') g n-1 (i ') + L -1 (n, i '). g not (not) for 0 ⁇ i ' ⁇ n.
• the calculation 214 is detailed in FIG. 11.
• x (( k ) b (n) serves as the initialization value for the variable tmq.
• the index i is also initialized to 0.
• the comparison 218 is then carried out between the integers i and n. If i ⁇ n, we add the term b (i). Linv (i) to the variable tmq and we increment i by one unit (step 220) before returning to the comparison 218.
• This loop includes a comparison 224 between the integers i 'and n.
• Step 226 the gain g (i') is recalculated in step 226 by adding Linv (i '). G (n) to its value calculated during the previous iteration n-1, then we subtract from target vector e the vector g (i '). F p (i') .
• Step 226 also includes the incrementation of the index i 'before returning to the comparison 224.
• Segmental pulse search significantly decreases the number of pulse positions to be evaluated during steps 182 of the search for stochastic excitation. It also allows efficient quantification of the positions found.
• ns> np also has the advantage that good robustness to transmission errors can be obtained with regard to the positions of the pulses, by virtue of a separate quantification of the sequence numbers of the occupied segments and of the relative positions pulses in each occupied segment.
• the possible binary words are stored in a quantification table in which the reading addresses are the quantization indexes received.
• the order in this table, determined once for all, can be optimized so that an error of transmission affecting a bit of the index (the error case the more frequent, especially when interlacing is used work in the channel encoder 22) has, on average, minimal consequences according to a neighborhood criterion.
• the neighborhood criterion is for example that a word of ns bits does not can be replaced only by words "neighbors", distant a Hamming distance at most equal to an np-2 ⁇ threshold, so as to keep all the pulses except ⁇ of them at valid positions in case of transmission error the single-bit index.
• Other criteria would be usable in substitution or in addition, for example that two words are considered neighbors if the replacement of one by the other does not change the order of assignment of gains associated with pulses.
• the order in the table word quantification can be determined from arithmetic considerations or, if this is insufficient, in simulating error scenarios on a computer (so exhaustive or by statistical sampling of the type Monte-Carlo according to the number of possible error cases).
• the module scheduling 46 can put in the category of minimum protection, or in the unprotected category, a nx number of index bits which, if they are affected by a transmission error, give rise to a wrong word but checking the neighborhood criterion with a probability considered satisfactory, and put in a category more protected the other bits of the index. This way of proceed uses another word order in the quantification table.
• This scheduling can also be optimized using simulations if you want maximize the number nx of the index bits assigned to the least protected category.
• One possibility is to start by constituting a list of words of ns bits by counting in Gray code from 0 to 2 ns -1, and to obtain the ordered quantification table by deleting from this list the words having no weight of Hamming of np.
• the table thus obtained is such that two consecutive words have a Hamming distance of np-2. If the indexes in this table have a binary representation in Gray code, any error on the least significant bit causes the index to vary by ⁇ 1 and therefore causes the replacement of the actual occupancy word by a neighboring word in the sense of the np-2 threshold on the Hamming distance, and an error on the i-th least significant bit also varies the index by ⁇ 1 with a probability of approximately 2 1-i .
• nx By placing the nx least significant bits of the index in Gray code in an unprotected category, a possible transmission error affecting one of these bits leads to the replacement of the busy word by a neighboring word with a probability at least equal. to (1 + 1/2 + ... + 1/2 nx-1 ) / nx. This minimum probability decreases from 1 to (2 / nb) (1-1 / 2 nb ) for nx increasing from 1 to nb.
• the errors affecting the nb-nx most significant bits of the index will most often be corrected thanks to the protection applied to them by the channel coder.
• the value of nx is in this case chosen according to a compromise between robustness to errors (small values) and a reduced size of the protected categories (large values).
• the possible binary words for represent the occupation of the segments are arranged in order growing in a search table.
• An indexing table associates with each address the serial number, in the table of quantization stored at the decoder, of the binary word having this address in the lookup table.
• the content of the table search and index table is given in the table III (in decimal values).
• the quantification of the occupation word of the segments deduced from the np positions provided by the stochastic analysis module 40 is carried out in two stages by the quantization module 44.
• a dichotomous search is first carried out in the search table to determine the address in this table of the word to be quantified.
• the quantization index is then obtained at the address determined in the indexing table and then supplied to the bit scheduling module 46.
• Address Search table Indexing table 0 3 0 1 5 1 2 6 5 3 9 2 4 10 4 5 12 3
• the module 44 also performs the quantification of the gains calculated by the module 40.
• the quantization bits of Gs are placed in a category protected by the channel 22 encoder, as well as most significant bits of the gain quantification indexes relative.
• the relative gain quantization bits are ordered to allow assignment to impulses associated belonging to the segments localized by the word of occupation. Segmental research according to the invention also allows effective protection of positions relative pulses associated with the largest values gain.
• the decoder 54 To reconstruct impulse contributions of excitation, the decoder 54 first locates the segments by means of the occupation word received; he then assigns the associated earnings; then he assigns the positions relative to pulses based on the order of importance of the gains.
• the 13 kbit / s speech coder requires order 15 million comma instructions per second (Mips) fixed. So we typically do this by programming a commercial digital signal processor (DSP) as well as the decoder which requires only about 5 Mips.
• DSP digital signal processor

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