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 synthesis 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.
• stochastic excitation consists of a vector searched for in a predetermined dictionary.
• 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 provide a speech coding method in which the search for stochastic excitation is simplified.
• the invention thus proposes a coding method with analysis by synthesis of a speech signal digitized in successive frames divided into sub-frames of lst samples, in which a linear prediction analysis is carried out for each frame to determine the coefficients of a short-term synthesis filter, and an excitation sequence with no contributions each associated with a respective gain is determined for each sub-frame so that the excitation sequence subjected to the short-term synthesis filter produces a synthetic signal representative of the speech signal, the ne contributions of the excitation sequence and the associated gains being determined by an iterative process in which the iteration n (0 ⁇ n ⁇ nc) comprises:
• F p denotes a line vector with lst components equal to the products of convolution between a possible value of the contribution n and the impulse response of a filter composed of the short-term synthesis filter and a weighting filter perceptual
• B n b n , where B n is a symmetric matrix with n + 1 rows and n + 1 columns whose component B n (i, j) (0 ⁇ i, j ⁇ n) is equal to the scalar product F p ( i) .F p (j) T where F p (i) and F p (j) respectively designate the line vectors equal to the convolution products between the contributions i and j previously determined and the impulse response of the compound filter, and b n is a line vector with n + 1 components b n (i) (0 ⁇ i ⁇ n) respectively equal to the scalar products between the vectors F p (i) and the initial target vector X,
• ne gains associated with ne contributions of the excitation sequence being those calculated during the nc-1 iteration.
• L n , R n and K n denote matrices with n + 1 rows and n + 1 columns corresponding respectively to the first n + 1 rows and to the first n + 1 columns of said matrices L, R and K, the matrices L and R being lower triangular, the matrix K being diagonal, and the matrix L having only 1 on its main diagonal
• we calculate the line n of the matrix L inverse of the matrix L, and we calculate the n + 1 gains according to the relation g n b n .K n .
• L n -1 designates the matrix with n + 1 rows and n + 1 columns corresponding respectively to the first N + 1 rows and to the n + 1 first columns of the inverse matrix L -1 .
• This excitation search mode limits the complexity of the calculations required to determine the excitation sequence, by making it possible to carry out only one division or inversion by iteration.
• the contributions can be impulse contributions.
• This excitation search mode is not however applicable exclusively to MPLPC coders. It is applicable for example to so-called VSELP coders where the 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). Furthermore, the contributions cannot include the contribution corresponding to the delayed excitation delayed by TP samples, whose associated gain g P is recalculated during successive iterations, or several contributions of this nature if several LTP delays are determined.
• FIG. 1 is a block diagram of a radio station incorporating a speech encoder implementing the invention
• FIG. 2 is a block diagram of a radio station capable of receiving a signal produced by that of Figure 1;
• FIGS. 3 to 6 are flowcharts illustrating an open loop LTP analysis process applied in the speech coder of Figure 1;
• FIG. 7 is a flowchart illustrating a process for determining the impulse response of the weighted synthesis filter applied in the speech coder of Figure 1;
• FIGS. 8 to 11 are flowcharts illustrating a process for finding the stochastic excitation applied in the speech coder of FIG. 1.
• 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 of the 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 of bit rate significantly lower than that of the speech signal S, and addresses this sequence to a channel coder 22 the function of which is to introduce redundancy bits into the signal in order to allow detection and / or correction of possible 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 synthesis analysis coder.
• the coder 16 determines on the one hand parameters characterizing a short-term synthesis filter modeling the speaker's vocal tract, and on the other hand an excitation sequence which, applied to the short-term synthesis filter, provides a synthetic signal constituting an estimate of the speech signal S according to a perceptual weighting criterion.
• the short-term synthesis filter has a transfer function of the form 1 / A (z), with:
• 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 representation of the prediction coefficients a i by LSP parameters is frequently used in speech coders with analysis by synthesis.
• 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 a spectral frequencies using Chebyshev polynomials", IEEE Trans. ASSP, Vol.34, No. 6, 1986, pages 14191426). 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 coefficients a i can be found simply, given that:
• LST t (nst-1) LSP t for sub -frames 0, 1, 2, ..., nst-1 of frame t.
• the coefficients a i of the filter 1 / A (z) are then determined, sub-frame by sub-frame from the interpolated LSP parameters.
• 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 signal SW which is analyzed by modules 36, 38, 40 to determine the excitation sequence.
• the excitation sequence of the short-term filter consists of an excitation predictable by a long-term synthesis filter modeling the pitch (pitch) of the speech, and a non-predictable stochastic excitation, or innovation sequence. .
• Module 36 performs long-term prediction
• the weighting filter 34 intervenes upstream of the open-loop analysis module, but it could be otherwise: the module 36 could operate directly on the speech signal S or even on the signal S cleared of its short-term correlations by a transfer function filter A (z).
• the modules 38 and 40 operate in closed loop, that is to say that they contribute directly to the minimization of the perceptually weighted error.
• a fractional resolution is provided for the smallest delay values so as to avoid discernible differences in terms of voicing frequency.
• We use for example a resolution 1/6 between rmin-21 and 33 + 5/6, a resolution 1/3 between 34 and 47 + 2/3, a resolution 1/2 between 48 and 88 + 1/2, and a integer resolution between 89 and rmax 142.
• the long-term prediction delay is determined in two stages.
• the open loop LTP analysis module 36 detects the voiced frames of the speech signal and determines, for each voiced frame, a degree of voicing MV and a search interval for the long-term prediction delay.
• the search interval is defined by a central value represented by its quantization index ZP and by a width in the domain of the quantization 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 quantification is for example vector, that is to say it consists in selecting, in one or more quantification tables predetermined, a set of quantized parameters LSP Q which has a minimum distance from the set of parameters LSP provided by the module 28.
• the quantization 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 filter composed of the short-term synthesis filter and the perceptual weighting filter.
• This compound filter has the transfer function W (z) / A (z).
• the module 42 takes for the perceptual weighting filter W (z) that corresponding to the LSP parameters interpolated but not quantified, that is to say the one whose coefficients were calculated by the module 32, and for the synthesis filter 1 / A (z) that corresponding to the LSP parameters quantized and interpolated, that is to say the one that will be effectively reconstructed by the decoder.
• the TP delay index is ZP + DP.
• closed-loop LTP analysis consists in determining, in the search interval for long-term prediction delays T, the delay TP which maximizes, for each sub-frame of a voiced frame, the normalized correlation:
• x (i) denotes the weighted speech signal SW of the subframe from which the memory of the weighted synthesis filter has been subtracted (i.e. the response to a zero signal, due to its initial states, of the filter whose impulse response was calculated by module 42), and y T (i) denotes the convolution product:
• u (j-T) designating the predictable component of the delayed excitation sequence of T samples, estimated by the well-known technique of the adaptive codebook. For delays T less than the length of a subframe, the missing values of u (j-T) can be extrapolated from the previous values. Fractional delays are taken into account by oversampling the signal u (j-T) in the adaptive repertoire. An oversampling of a factor m is obtained by means of polyphase interpolating filters.
• the gain g P of long-term prediction could be determined by the module 38 for each sub-frame, by applying the known formula:
• 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.
• An innovation sequence of lst samples comprises np pulses of positions p (n) and of amplitude g (n).
• the positions and gains calculated by the module 40 of stochastic analysis are quantified by a module 44.
• a bit scheduling module 46 receives the various parameters which will be useful to the decoder, and constitutes the binary sequence transmitted to the channel coder 22. These parameters are:
• a module 48 is thus provided in the encoder which receives the various parameters and which adds to some of them redundancy bits making it possible to detect and / or correct any transmission errors. For example, 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 the module 48. It is for example possible to add a parity bit to the two bits coding MV and to repeat once the three bits thus obtained. This example of redundancy makes it possible to detect all the single or double errors and to correct all the simple errors and 75% of the double errors.
• the allocation of the bit rate per 20 ms frame is for example that indicated in Table I.
• the channel coder 22 is that used in the pan-European system of radiocommunication with mobiles (GSM).
• GSM pan-European system of radiocommunication with mobiles
• This channel coder described in detail in Recommendation GSM 05.03, was developed for a 13 kbit / s speech coder of RPE-LTP type which also produces 260 bits per 20 ms frame. The sensibility of each of the 260 bits was determined from listening tests.
• the bits from the source encoder have been grouped into three categories. The first of these categories IA groups 50 bits which are coded convolutionally on the basis of a generator polynomial giving a redundancy of one half with a constraint length equal to 5. Three parity bits are calculated and added to the 50 bits of the category IA before convolutional coding.
• the second category (IB) has 132 bits which are protected at a rate of a half by the same polynomial as the previous category.
• the third category (II) contains 78 unprotected bits. After application of the convolutional code, the bits (456 per frame) are subjected to interleaving.
• 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 diagrammatically in FIG. 2.
• the received radio signal is first processed by a demodulator 50 then by a channel decoder 52 which performs the dual operations those of the modulator 24 and of the channel coder 22.
• the channel decoder 52 supplies the speech decoder 54 with a binary sequence which, in the absence of transmission errors or when the possible errors have been corrected by the channel decoder 52, corresponds to the binary sequence delivered by the scheduling module 46 at the level of the coder 16.
• the decoder 54 comprises a module 56 which receives this binary sequence 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, the module 56 examines the redundancy bits introduced by the module 48 of the coder, to detect and / or correct the errors affecting the parameters associated with these redundancy bits.
• 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 subframe, 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 also 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.
• the estimate P st (k) expressed in decibels is written:
• 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 resolved maximum tion 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 90 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 index i at address j in the list I st , the value m is given to the integer m0 intended to be equal to the index of the smallest submultiple retained, then the address j is incremented by one.
• 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 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 l st in the order of gains C st 2 (r Ist (j) ) / G st 2 (r Is t (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 ZP, ZP0 and ZP1 indexes in a phase 132, the progress of which is detailed in FIG. 6.
• This phase 132 consists in testing search intervals of length NI 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 l 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, the address j is directly incremented in step 140, then it is compared 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.
• step 152 is executed before incrementing the address j in step 140.
• the index ZP is taken equal to I st (j) and the indexes ZP0 and ZP1 are respectively taken equal to the smallest and the largest of the indexes i st . determined in step 148.
• the index st is incremented by one unit (step 154) then compared, in step 156, to the number nst of sub-frames per frame. If st ⁇ nst, we return to step 98 to carry out the operations relating to the next sub-frame.
• the index ZP designates the center of the search interval which will be supplied to the module 38 of LTP analysis in closed loop
• ZP0 and ZP1 are indices whose deviation is representative the dispersion of the optimal delays by sub-frame 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.
• the ZP + DP index of the TP delay finally determined can therefore in some cases be smaller than 0 or greater than 255. This allows the closed-loop LTP analysis to also relate to some TP delays smaller than rmin or more larger than rmax. This improves the subjective quality of the reproduction of so-called pathological voices and non-voice signals (DTMF voice frequencies or signaling frequencies used by the switched telephone network).
• 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.
• the autocorrelations C (k) and the delayed energies G (k) for the entire frame are also calculated:
• P (K) 20.log 10 [R0 / [R0-X (K)]].
• 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 optimization steps 148, the index i st is determined on the one hand, which maximizes C st ' 2 (r i ) / G sf' (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 Ist (j) -N3 / 2 ⁇ i ⁇ I st (j) + N3 / 2 and 0 ⁇ i ⁇ N.
• Step 152 is also modified: the ZP0 indexes are no longer stored and
• Gp' 20. log 10 [R0 / (R0-Ymax ')].
• a fourth variant of the open loop LTP analysis process mainly concerns weakly voiced frames
• MV 1).
• These frames often correspond to a start or an end of a voicing area. Frequently, these frames can comprise from one to three sub-frames for which the gain coefficient of the long-term synthesis filter is zero or even negative. It is proposed not to perform LTP analysis in closed loop for the sub-frames in question, in order to reduce the average complexity of the coding. This can be achieved by storing in step 152 of FIG. 6 nst pointers indicating for each subframe st 'whether the autocorrelation C st , corresponding to the index delay i st , is negative or even very small.
• the sub-frames for which the prediction gain is negative or negligible can be identified by consulting the nst pointers. If applicable, module 38 is deactivated for subframes corresponding. 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 truncated energies of the impulse response are also calculated:
• the components h (i) of the impulse response and the truncated energies Eh (i) can be obtained by filtering a unitary pulse by means of a transfer function filter W (z) / A (z) of zero initial states , or by recurrence:
• the coefficients a i are those used in the perceptual weighting filter, i.e. the linear prediction coefficients interpolated but not quantified, while in expression (3), the coefficients ai. 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 truncated impulse response to L ⁇ samples is at least equal to a proportion ⁇ of its total energy Eh (pst-l) 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 test 164 shows that Eh (L ⁇ -2) ⁇ a. 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 length of truncation Lh of the impulse response is taken equal to L ⁇ if L ⁇ snst and to nst otherwise.
• a third aspect of the invention relates to the stochastic analysis module 40 used to model the unpredictable part of the excitation.
• the stochastic excitation considered here is of the multi-pulse type.
• Stochastic excitement 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 ne contributions associated respectively with ne 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.
• One of the contributions can be predictable, or several in the case of a long-term synthesis filter with several takes ("multi-tap pitch synthesis filter").
• the other contributions are in the present case np vectors comprising only 0 except an impulse of amplitude 1.
• the line vectors F p (n) (0 ⁇ n ⁇ nc) are weighted contributions having as components i (0 ⁇ i ⁇ lst) the products of convolution between the contribution n to the excitation sequence and the impulse response h from the filter weighted summary;
• 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 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 stochastic analysis relating to a subframe of a voiced frame can therefore take place as indicated in FIGS. 8 to 11.
• the contribution index n is initialized to 0 in step 180 and the vector F p (0) is taken equal to the long-term contribution Y TP provided by the module 38. If n> 0, the iteration n begins with the determination 182 of the position p (n) of the pulse n which maximizes the quantity:
• e (e (0), ..., e (lst-1)) is a target vector calculated during the previous iteration.
• the maximization of (F p .e T ) 2 / (F p .F p T ) is performed on all the possible positions p in the subframe.
• the maximization is carried out in step 182 on the set of possible positions excluding the segments in which the positions p (1), ..., p (n have been found respectively) -1) pulses during previous iterations.
• 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.
• the decomposition of the matrix B makes it possible to write: for the component located in row n and in column j. We can therefore write, for j increasing from 0 to n-1:
• the column index j is first initialized at 0, in step 186.
• the variable tmp is first initialized at the value of component B (n, j), that is:
• step 188 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.
• step 196 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.
• K (n) is taken equal to 1 / tmp if tmp ⁇ 0 (step 198) and to 0 otherwise.
• the calculation 184 requires at most one division 198, to obtain K (n).
• any singularity of the matrix B n does not cause instabilities since we avoid divisions by 0.
• step 204 the term Linv (j ') is initialized to -L (n, j ") and the integer k' to j '+ 1.
• a comparison 206 is then made 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.
• One can indeed compute the vector g n (g n (0), ..., g n (n)) solution of g n .
• B n b n according to: and
• g n (i ') g n-1 (i') + L -1 ⁇ n, i ') ⁇ g n (n) for 0 ⁇ i' ⁇ n.
• the calculation 214 is detailed in FIG. 11.
• the component b (n) of the vector b is first calculated:
• 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.
• 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 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 quantization table in which the read addresses are the received quantization indexes.
• the order in this table determined once and for all, can be optimized so that a transmission error affecting a bit of the index (the most frequent error case, especially when an interleaving is implemented in the channel coder 22) has, on average, minimal consequences according to a neighborhood criterion.
• the neighborhood criterion is for example that a word of ns bits can only be replaced by "neighboring" words, distant from a Hamming distance at most equal to a threshold np-2 ⁇ , so as to keep all the pulses except ⁇ of them at valid positions in the event of an error in transmission of the index relating to a single bit.
• Other criteria could be used in substitution or in addition, for example that two words are considered to be neighbors if the replacement of one by the other does not modify the order of allocation of the gains associated with the pulses.
• the order in the word quantification table can be determined from arithmetic considerations or, if this is insufficient, by simulating the error scenarios on a computer (exhaustively or by statistical sampling of the type Monte-Carlo according to the number of possible error cases).
• simulating the error scenarios on a computer it is also possible to take advantage of the different protection categories offered by the channel encoder 22, in particular if the neighborhood criterion cannot be satisfactorily verified for all cases. possible errors affecting a bit of the index.
• the scheduling module 46 can thus put in the minimum protection category, or in the unprotected category, a certain number nx of the bits of the index which, if affected by a transmission error, give rise to a wrong word but checking the neighborhood criterion with a probability deemed satisfactory, and putting the other bits of the index in a more protected category.
• This procedure calls for a different ordering of the words in the quantification table.
• This scheduling can also be optimized by means of simulations if it is desired to maximize the number nx of the bits of the index 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 bits makes vary the index of ⁇ 1 and thus involves the replacement of the words of effective occupation by a neighbor 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 .
• 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.
• nx (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 to represent the occupation of the segments are arranged in ascending order in a search table.
• An indexing table associates with each address the serial number, in the quantification table stored at the decoder, of the binary word having this address in the search table.
• the content of the search table and of the indexing table is given in 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.
• the module 44 also performs the quantification of the gains calculated by the module 40.
• the greatest absolute value Gs of the gains g (1), ..., g (np) is quantified over 5 bits, for example by taking 32 quantization values in geometric progression in the interval [0; 32767], and each of the relative gains g (1) / Gs, ..., g (np) / Gs in the interval is quantified
• the quantization bits of Gs are placed in a category protected by the channel coder 22, as are the most significant bits of the quantization indexes of the relative gains.
• the relative gain quantization bits are ordered so as to allow their assignment to the associated pulses belonging to the segments located by the busy word.
• the segmental search according to the invention also makes it possible to effectively protect the relative positions of the pulses associated with the greatest gain values.
• the decoder 54 To reconstruct the impulse contributions of the excitation, the decoder 54 first locates the segments by means of the occupation word received; he then attributes the associated winnings; then it assigns the positions relative to the impulses on the basis of the order of importance of the gains.
• the 13 kbit / s speech coder requires around 15 million instructions per second (Mips) in fixed point. This will therefore typically be done by programming a commercial digital signal processor (DSP), as well as the decoder which requires only around 5 Mips.
• DSP digital signal processor

Landscapes

• Engineering & Computer Science (AREA)
• Computational Linguistics (AREA)
• Signal Processing (AREA)
• Health & Medical Sciences (AREA)
• Audiology, Speech & Language Pathology (AREA)
• Human Computer Interaction (AREA)
• Physics & Mathematics (AREA)
• Acoustics & Sound (AREA)
• Multimedia (AREA)
• Compression, Expansion, Code Conversion, And Decoders (AREA)
• Investigating Or Analysing Materials By Optical Means (AREA)
• Analysing Materials By The Use Of Radiation (AREA)

Applications Claiming Priority (3)

Publications (2)

Family

ID=9474923

Family Applications (2)

Family Applications After (1)

Country Status (8)

Families Citing this family (19)

Family Cites Families (23)

• 1995
  • 1995-01-06 FR FR9500124A patent/FR2729244B1/fr not_active Expired - Fee Related
• 1996
  • 1996-01-03 AT AT96901009T patent/ATE174147T1/de not_active IP Right Cessation
  • 1996-01-03 EP EP96901009A patent/EP0801789B1/de not_active Expired - Lifetime
  • 1996-01-03 DE DE69601068T patent/DE69601068T2/de not_active Expired - Fee Related
  • 1996-01-03 US US08/860,799 patent/US5899968A/en not_active Expired - Fee Related
  • 1996-01-03 AU AU44902/96A patent/AU4490296A/en not_active Abandoned
  • 1996-01-03 WO PCT/FR1996/000005 patent/WO1996021219A1/fr active IP Right Grant
  • 1996-01-03 CN CNB961917954A patent/CN1134761C/zh not_active Expired - Fee Related
  • 1996-01-05 AT AT96400028T patent/ATE183600T1/de not_active IP Right Cessation
  • 1996-01-05 DE DE69603755T patent/DE69603755T2/de not_active Expired - Fee Related
  • 1996-01-05 EP EP96400028A patent/EP0721180B1/de not_active Expired - Lifetime

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events