EP0608174B1 - System zur prädiktiven Kodierung/Dekodierung eines digitalen Sprachsignals mittels einer adaptiven Transformation mit eingebetteten Kodes - Google Patents

System zur prädiktiven Kodierung/Dekodierung eines digitalen Sprachsignals mittels einer adaptiven Transformation mit eingebetteten Kodes Download PDF

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EP0608174B1
EP0608174B1 EP94400109A EP94400109A EP0608174B1 EP 0608174 B1 EP0608174 B1 EP 0608174B1 EP 94400109 A EP94400109 A EP 94400109A EP 94400109 A EP94400109 A EP 94400109A EP 0608174 B1 EP0608174 B1 EP 0608174B1
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Prior art keywords
signal
module
perceptual
speech signal
vector
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French (fr)
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EP0608174A1 (de
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Bruno Lozach
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Orange SA
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France Telecom SA
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    • 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
    • G10L19/02—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 spectral analysis, e.g. transform vocoders or subband vocoders
    • G10L19/0212—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 spectral analysis, e.g. transform vocoders or subband vocoders using orthogonal transformation
    • 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
    • G10L2019/0001—Codebooks
    • G10L2019/0002—Codebook adaptations
    • 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
    • G10L2019/0001—Codebooks
    • G10L2019/0003—Backward prediction of gain
    • 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
    • G10L2019/0001—Codebooks
    • G10L2019/0004—Design or structure of the codebook
    • G10L2019/0005—Multi-stage vector quantisation
    • 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
    • G10L2019/0001—Codebooks
    • G10L2019/0011—Long term prediction filters, i.e. pitch estimation

Definitions

  • the present invention relates to a system for predictive coding-decoding of a digital speech signal by adaptive transform with nested codes.
  • the digital signal to code Sn coming from a signal of analog source speech, is subject to a process of short-term prediction, LPC analysis, coefficients of prediction being obtained by prediction of the speech signal on windows with M samples.
  • the signal speech digital code Sn is filtered using a perceptual weighting filter W (z) deduced from the coefficients above, to get the signal perceptual pn
  • a long-term prediction process then makes it possible to take into account the periodicity of the residue for the voiced sounds, on all the sub-windows of N samples, N ⁇ M, in the form of a contribution p and n , which is subtracted from the perceptual signal pn so as to obtain the signal p'n in the form of a vector P' ⁇ R N.
  • a transformation followed by a quantification are then carried out on the aforementioned vector P ′ in order to carry out a digital transmission.
  • the reverse operations allow, after transmission, the modeling of the synthetic signal S and n .
  • the Karhunen-Loeve transform obtained from the eigenvectors of the auto-correlation matrix where I is the number of vectors contained in the learning corpus, allows to maximize the expression where K is an integer, K ⁇ N.
  • K is an integer
  • K ⁇ N the mean square error of the Karhunen-Loeve transform is lower than that of any other transformation for a given modeling order K, this transform being, in this sense, optimal .
  • This type of transform was introduced into a predictive coder by orthogonal transform by N. Moreau and P. Dymarski, confer publication "Successive Orthogonalisations in the Multistage CELP Coder", ICASSP 92 Vol.1, pp I-61 - I-64.
  • suboptimal transforms such as the transform of Fourier Rapide (FFT), the transform into a discrete cosine (TCD) the discrete transform of Hadamard (DHT) or Walsh Hadamard (DWHT) for example.
  • FFT Fourier Rapide
  • TCD discrete cosine
  • DHT discrete transform of Hadamard
  • DWHT Walsh Hadamard
  • Another method for the construction of an orthonormal transform consists in decomposing into singular values the lower triangular Toeplitz matrix H defined by: matrix in which h (n) is the impulse response of the short-term prediction filter 1 / A (z) of the current window.
  • the matrix H can then be decomposed into a sum of matrices of rank 1:
  • Encoders with nested codes currently known allow data to be transmitted by theft of elements binaries normally allocated to speech on the channel transmission in a transparent manner for the encoder, which encodes the speech signal at the maximum rate.
  • a 64 kbit / s encoder with scaled quantizer with nested codes has been normalized to 1986 by standard G 722 established by the CCITT.
  • This encoder operating in the field of wideband speech (50 Hz to 7 kHz bandwidth audio signal, sampled at 16 kHz), is based on coding in two sub-bands each containing a Pulse Modulation encoder and Adaptive Differential Coding (MICDA coding).
  • MICDA coding Adaptive Differential Coding
  • This coding technique allows you to transmit signals from wideband speech and data, if necessary, on a 64 kbit / s channel, at three different bit rates 64-56-48 kbit / s and 0-8-16 kbit / s for data.
  • Coders predictive by art transformation above do not allow the transmission of data and therefore cannot fulfill the function of coders with nested codes.
  • coded coders nested in the prior art do not use the technique of the orthonormal transform, which does not allow to tend towards or reach an optimal transform coding.
  • the object of the present invention is to remedy the aforementioned drawback by the implementation of a system of predictive coding-decoding of a digital speech signal by adaptive transform with nested codes.
  • Another object of the present invention is the implementation implementation of a predictive coding-decoding system of a digital speech and data signal allowing transmission at reduced and flexible rates.
  • the predictive coding system for a digital signal into a digital signal with nested codes in which the coded digital signal consists of a speech signal encoded and, if necessary, by an auxiliary data signal inserted into the coded speech signal after coding this last object of the present invention includes a filter perceptual weighting driven by a prediction loop short term to generate a perceptual signal and a long-term prediction circuit delivering a estimated perceptual signal, this long prediction circuit term forming a long-term prediction loop allowing to deliver, from the perceptual signal and the signal estimated past excitation, a perceptual excitation signal modeled and adaptive transform circuits and quantization allowing from the excitation signal perceptual to generate the coded speech signal.
  • the weighting filter perceptual consists of a prediction filter to short term of the speech signal to be coded, so as to achieve a frequency distribution of the quantization noise and in that it includes a circuit for subtracting the contribution of the excitation signal passed from the signal perceptual to deliver an updated perceptual signal, the long-term prediction circuit being formed, in a loop closed, from a dictionary updated by excitement past modeled corresponding to the lowest flow to deliver an optimal waveform and gain associated with it, constituting the estimated perceptual signal.
  • the transform circuit is formed by a module of orthonormal transform including a transformation module orthogonal adaptive and a modeling module progressive by orthogonal vectors.
  • the modeling module progressive and long-term prediction circuit allow the delivery of indexes representative of the signal coded speech.
  • An auxiliary data insertion circuit is coupled to the transmission channel.
  • the transform predictive decoding system adaptive of a digital signal coded with codes nested in which the coded digital signal consists of a signal digital coded and, where appropriate, by a data signal auxiliaries inserted in the coded speech signal after coding of the latter, is remarkable in that it comprises a data signal extraction circuit enabling, hand, data extraction for use auxiliary and, on the other hand, index transmission representative of the coded speech signal. It includes in addition to a circuit for modeling the speech signal at minimum flow and a signal modeling circuit speech at least at a rate greater than the minimum rate.
  • the predictive coding-decoding system for a signal speech digital by adaptive transform to codes nested object of the present invention finds application, in general, to the transmission of speech and data at flexible rates, and more specifically, audio-visual conference protocols, videophone, speakerphone, storage and transport of digital audio signals over links long distances, when transmitting with mobiles and channel concentration systems.
  • the signal digital coded by implementing the coding system object of the present invention consists of a signal speech coded and if necessary by a data signal auxiliaries inserted in the coded speech signal, after coding of this digital speech signal.
  • the object coding system of the present invention may include, from a transducer delivering the analog speech signal, a converter analog-digital and a memory circuit input or input buffer to deliver the signal digital to code Sn.
  • the coding system object of the present invention also comprises a perceptual weighting filter 11 controlled by a short-term prediction loop making it possible to generate a perceptual signal, noted .
  • the long-term prediction circuit 13 forms a long-term prediction loop to deliver, to from the perceptual signal and the past excitation signal estimated, noted P and 0 / n, a perceptual excitation signal modeled.
  • the coding system which is the subject of the invention as shown in FIG. 2 further comprises an adaptive transform and quantization circuit making it possible, from the perceptual excitation signal P n, to generate the coded speech signal as it will be described below in the description.
  • the perceptual weighting filter 11 consists of a filter for short-term prediction of the speech signal to be coded, so as to achieve a frequency distribution of the quantization noise.
  • the perceptual weighting filter 11 delivering the perceptual signal thus comprises, as shown in the same FIG. 2, a circuit 120 for subtracting the contribution of the past excitation signal P and 0 / n from the perceptual signal to deliver an updated perceptual signal, this perceptual signal updated being noted P n .
  • the long term prediction circuit 13 is formed in a closed loop from an updated dictionary by the past excitation modeled corresponding to the lowest bit rate, this dictionary allows to deliver an optimal waveform and an estimated gain associated therewith.
  • this dictionary allows to deliver an optimal waveform and an estimated gain associated therewith.
  • the corresponding modeled past excitation at the lowest flow rate is noted r and 1 / n.
  • the transform module circuit is formed by an orthonormal transform module 14, comprising a properly adaptive orthogonal transformation module said and a progressive modeling module by orthogonal vectors, noted 16.
  • the progressive modeling module 16 and the circuit long-term prediction 13 allow to issue indexes representative of the coded speech signal, these indexes being denoted i (0), j (0) respectively i (1), j (1) with 1 ⁇ [1, L] in figure 2.
  • the coding system further includes a data insertion circuit 19 auxiliaries coupled to the transmission channel, noted 18.
  • the operation of the object coding device the present invention can be illustrated as follows.
  • the synthetic signal S and n is of course the signal reconstructed on reception, that is to say at the decoding level after transmission as will be described later in the description.
  • a short-term prediction analysis formed by the analysis circuit 10 of the LPC type for "Linear Predictive Coding" and by the perceptual weighting filter 11 is carried out for the digital signal to be coded by a conventional prediction technique on windows comprising for example M samples.
  • the analysis circuit 10 then delivers the coefficients a i , where the aforementioned coefficients a i are the linear prediction coefficients.
  • the speech signal to be coded Sn is then filtered by the perceptual weighting filter 11 of transfer function W (z), which makes it possible to deliver the perceptual signal proper, noted .
  • the coefficients of the perceptual weighting filter are obtained from a short-term prediction analysis on the first correlation coefficients of the sequence of the coefficients a i of the analysis filter A (z) of circuit 10 for the current window.
  • This operation makes it possible to achieve a good frequency distribution of the quantization noise.
  • the perceptual signal delivered tolerates greater coding noise in high-energy areas where the noise is less audible, since it is frequently masked by the signal. It is indicated that the perceptual filtering operation is broken down into two stages, the digital signal to code Sn being filtered a first time by the filter constituted by the analysis circuit 10, in order to obtain the residue to be modeled, then a second times by the perceptual weighting filter 11 to deliver the perceptual signal .
  • the second operation is to then remove the contribution from the excitement past, or estimated past excitation signal, noted p and 0 / n of aforementioned perceptual signal.
  • h n is the impulse response of the double filtering performed by the circuit 10 and the perceptual weighting filter 11 in the current window and r and 1 / n is the past excitation modeled corresponding to the lowest flow rate, as well as 'It will be described later in the description.
  • the operating mode of the long prediction circuit term 13 in closed loop is then the following.
  • This circuit allows to take into account the periodicity of the residue for voiced sounds, this long-term prediction being performed all the sub-windows of N samples, thus that it will be described in connection with FIG. 3.
  • the long-term closed-loop prediction circuit 13 comprises a first stage constituted by an adaptive dictionary 130, which is updated all the aforementioned sub-windows by the modeled excitation denoted r and 1 / n, delivered by the module 17 , which will be described later in the description.
  • the adaptive dictionary 130 makes it possible to minimize the error, noted with respect to the two parameters g 0 and q.
  • the waveform of index j is filtered by a filter 131 and corresponds to the excitation modeled at the lowest rate r and 1 / n delayed by q samples by the aforementioned filter.
  • the optimal waveform f 1 / n is delivered by the filtered adaptive dictionary 133.
  • a module 132 for calculating and quantifying the prediction gain makes it possible, from the perceptual signal P n and all the waveforms fj (0) / n, to carry out a calculation for quantifying the prediction gain, and to deliver an index i (0) representative of the number of the quantization range, as well as its associated quantized gain g (0).
  • a multiplier circuit 134 delivers from adaptive dictionary filtered 133, that is to say of the result filtering the waveform with index j C j / n, i.e. f j / n, and the associated quantified gain g (0), the prediction excitation at long term modeled and filtered perceptually noted P and 1 / n.
  • a module 136 makes it possible to calculate the Euclidean norm
  • a module 137 makes it possible to search for the optimal waveform corresponding to the minimum value of the above-mentioned Euclidean standard and to deliver the index j (0).
  • the parameters transmitted by the coding system object of the invention for modeling the long-term prediction signal are then the index j (0) of the optimal waveform f j (0) as well as the number i ( 0) of the quantization range of its associated gain g (0) quantized.
  • the method used for construction of this transform corresponds to that proposed by B.S.Atal and E.Ofer, as previously mentioned in the description.
  • this consists in decomposing, not the short-term prediction filtering matrix, but the perceptual weighting matrix W formed by a lower triangular Toeplitz matrix defined by the relation (4):
  • w (n) denotes the response of the perceptual weighting filter W (z) of the current window previously mentioned.
  • FIG. 4a there is shown the partial diagram of a predictive transform coder and in FIG. 4b, the corresponding equivalent scheme in which the matrix or perceptual weighting filter W, designated by 140, has been highlighted, a perceptual weighting filter reverse 121 having, however, been inserted between the long-term prediction 13 and the subtractor circuit 120. It is indicated that the filter 140 performs a combination linear of the basic vectors obtained from a decomposition into singular values of the matrix representative of the perceptual weighting filter W.
  • the signal S ' corresponding to the speech signal to be coded S n from which it has been subtracted the contribution of the past excitation delivered by the module 12, as well as that of the long-term prediction P and 1 / n filtered by a perceptual transfer weighting module with transfer function (W (z)) -1 , is filtered by the perceptual weighting filter with transfer function W (z), so as to obtain the vector P ' .
  • the matrix W is then decomposed into a sum of matrices of rank 1, and verifies the relation:
  • the short-term analysis filter circuit 10 being updated on windows of M samples, the decomposition into singular values of the matrix of W perceptual weighting is performed at the same frequency.
  • the orthonormal transform process is built by learning.
  • the orthonormal transform module can be formed by a stochastic transform submodule constructed by drawing of a Gaussian random variable for initialization, this sub-module comprising in FIG. 5 the steps 1000, 1001, 1002 and 1003 and being noted SMTS.
  • Step 1002 can consist in applying the algorithm of the K-mean over the aforementioned vector corpus.
  • the SMTS sub-module is successively followed by module 1004 for building centers, a module 1005 for construction of classes and, in order to obtain a vector G whose components are relatively ordered, of a transform reordering module 1006 according to the cardinal of each class.
  • the aforementioned module 1006 is followed by a module of Gram-Schmidt calculation, noted 1007a, so as to obtain a orthonormal transform.
  • the aforementioned module 1007a is associated a module 1007b for calculating the error under the conditions conventional implementation of the treatment process Gram-Schmidt.
  • the 1007a module is itself followed by a 1008 module test on the number of iterations, this to allow obtain an orthonormal transform performed offline by learning.
  • memory 1009 of memory type dead allows to memorize the orthonormal transform under transform vector shape. It is indicated that the scheduling relative of the components of the gain vector G is accentuated by the orthogonalization process.
  • Figure 5b shows the scheduling of components of the gain vector G, i.e. the value G normalized mean for a transform obtained on the one hand by decomposition into singular values of the matrix of perceptual weighting W, and on the other hand, by learning.
  • the orthonormal transform F can be obtained according to two different methods.
  • the new dimension of the gain vector G becomes then equal to N-1, which increases the number bits per sample during quantization vector of it and therefore the quality of its modelization.
  • a first solution to calculate the transform F 'can then consist in making a prediction analysis at long term, to shift the transform obtained by learning up a notch, to place the long-term predictor at the first position, then apply the Gram-Schmidt algorithm, in order to obtain a new transform F '.
  • the transformation used must keep the dot product.
  • the transformation is not applied than to the perceptual signal P, and the modeled perceptual signal P and can then be calculated by the inverse transformation.
  • the adaptive transformation module 14 can include a Householder transformation module 140 receiving the estimated perceptual signal constituted by the optimal waveform and by the estimated gain and the signal perceptual P to generate a transformed perceptual signal P ''. It is indicated that the Householder transformation module 140 comprises a module 1401 for calculating parameters B and wB as defined previously by relation 13. It also includes a module 1402 comprising a multiplier and a subtractor making it possible to carry out the transformation proper according to the relation 14. It is indicated that the transformed perceptual signal P '' is delivered in the form of a vector of perceptual transformed signal of component P '' k , with k ⁇ [0, N-1].
  • the adaptive transformation module 14 such as represented in FIG. 7 also includes a plurality N of registers for memorizing orthonormal waveforms, the current register being noted r, with r ⁇ [1, N].
  • the aforementioned N storage registers form the read-only memory previously described in the description, each register comprising N storage cells, each component of rank k of each vector, component denoted f 1 / orth (k) being stored in a row cell correspondent of the current register r considered.
  • the module 14 comprises a plurality of N multiplier circuits associated with each register of rank r forming the plurality of the previously mentioned storage registers.
  • each rank multiplier register k receives on the one hand the component of rank k of the stored vector and on the other hand the component P '' k of the transformed perceptual signal vector of corresponding rank k.
  • the Mrk multiplier circuit delivers the product P '' k .fk / orth (k) of the components of the transformed perceptual signal.
  • a plurality of N-1 summing circuits is associated with each register of rank r, each circuit summator of rank k, noted Srk, receiving the product of rank anterior k-1, and the product of corresponding rank k delivered by the multiplier circuit Mrk of the same rank k.
  • the circuit summator of highest rank, SrN-1 then delivers a component g (r) of the estimated gain expressed as a vector G gain
  • the progressive modeling module by orthogonal vectors in fact comprises a module 15 for normalizing the gain vector to generate a normalized gain vector, denoted G k , by comparison of the normalized value of the gain vector G with respect to a threshold value.
  • This normalization module 15 also makes it possible to generate a signal of length of the normalized gain vector linked to the modeling order k to the decoder system as a function of this modeling order.
  • the progressive vector modeling module orthogonal further comprises, in cascade with the module 15 normalization of the gain vector, a stage 16 of progressive modeling by orthogonal vectors.
  • This floor model 16 receives the normalized vector Gk and delivers the indexes representative of the coded speech signal, these indexes being denoted I (1), J (1), these indexes being representative selected vectors and their associated gain.
  • the transmission of auxiliary data formed by indexes is performed by overwriting the parts of the allocated frame to the indices and track numbers to form the signal auxiliary data.
  • the operation of the standardization module 15 is the following.
  • the gain vector thus obtained G K is then quantified and its length k is transmitted by the coding system object of the invention in order to be taken into account by the corresponding decoding system, as will be described later in the description.
  • the average standard criterion according to the order of modeling K is given in figure 8a for a transform orthonormal obtained on the one hand by decomposition into values singulars of the perceptual weighting matrix W and on the other hand by learning.
  • FIG. 8b A particularly advantageous embodiment of the progressive modeling module using orthogonal vectors 16 will now be given in connection with FIG. 8b.
  • the aforementioned module allows in fact to perform a quantification vector illustration.
  • ⁇ 1 is the gain associated with the optimal vector ⁇ j (1) / K from the stochastic dictionary of rank l, noted 16 l.
  • the vectors selected iteratively are generally not linearly independent and do not therefore not form a basis.
  • the subspace generated by the L optimal vectors ⁇ j (L) / K is of dimension less than L.
  • r (l, j) ⁇ ⁇ j (l) orth (l) ⁇ j (l) l
  • ⁇ j orth (l) > represents the intercorrelation of the optimal vectors of rank j and of rank j (l)
  • T K I NOT - ⁇ j (l) orth (l) ⁇ j (l) l ( ⁇ j orth (l) ) T represents the orthogonalization matrix.
  • the previous operation allows to remove from the dictionary the contribution of the previously selected wave and thus imposes linear independence for any vector optimal of rank i between l + 1 and L compared to optimal vectors of lower rank.
  • Q is an orthonormal matrix
  • R an upper triangular matrix whose elements of the main diagonal are all positive, which ensures the uniqueness of the decomposition.
  • the upper triangular matrix R thus makes it possible to recursively calculate the gains ⁇ (k) relative to the base of origin.
  • the parameters transmitted by the coding system which is the subject of the invention for modeling the gain vector G are then the indices j (l) of the selected vectors as well as the numbers i (l) of the ranges of quantification of their associated gains, .
  • Data transmission is then done by overwriting the parts of the frame allocated to the indices and track numbers j (l), i (l), for l ⁇ [L1, L2-1] and [L2, L] as required. of communication.
  • the previously mentioned processing process uses the recursive modified Gram-Schmidt algorithm in order to code the gain vector G.
  • the parameters transmitted by the coding system according to the invention being the aforementioned indices, j (0) to j (L ) of the different dictionaries as well as the quantified gains g (0) and ⁇ ⁇ , it is necessary to code the various aforementioned gains g (0) and ⁇ ⁇ .
  • a study has shown that the gains relative to the orthogonal base ⁇ j (l) / orth (L) ⁇ being decorrelated, these have good properties for their quantification.
  • the gains ⁇ ⁇ are ordered in a relatively decreasing order, and it is possible to use this property by coding not the aforementioned gains, but their ratio given by Several solutions can be used to code the aforementioned reports.
  • the coding device object of the present invention comprises a module for modeling the excitation of the filter summary corresponding to the lowest throughput, this module being noted 17 in the aforementioned figure.
  • the principle diagram for calculating the excitation signal of the synthesis filter corresponding to the lowest bit rate is given in FIG. 11.
  • An inverse transformation is applied to the gain vectors modeled G and 1 , this inverse adaptive transformation can for example correspond to a reverse transformation of the Householder type, which will be described later in the description, in conjunction with the decoding device which is the subject of the present invention.
  • the signal obtained after inverse adaptive transformation is added to the long-term prediction signal B '1 / n by means of a summator 171, the estimated perceptual signal or long-term prediction signal being delivered by the long-term prediction circuit 13 closed loop term.
  • the resulting signal delivered by the adder 171 is filtered by a filter 172, which corresponds from the point of view of the transfer function to the filter 131 of FIG. 3.
  • the filter 172 delivers the residual signal modeled r and 1 / n.
  • a transformative predictive decoding system adaptive to nested codes of a coded digital signal consisting of a coded speech signal, and where appropriate, by an auxiliary data signal inserted into the signal speech coded after coding of the latter will now described in conjunction with Figure 12.
  • the decoding system comprises a circuit 20 for extracting the data signal allowing on the one hand the extraction of the data for an auxiliary use, by an output of the auxiliary data and, on the other hand , the transmission of indexes representative of the coded speech signal.
  • the aforementioned indexes are the indices i (l) and j (l), for l between 0 and L 1 -1 previously described in the description and for l between l 1 and L under the conditions which will be described below.
  • the decoding system according to the invention comprises a circuit 21 for modeling the speech signal at the minimum bit rate, as well as a circuit 22 or 23 for modeling the speech signal at at least one flow greater than the aforementioned minimum flow.
  • the decoding system comprises, in addition to the system for extracting data 20, a first signal modeling module 21 speech at minimum bit rate receiving signal directly coded and delivering a first estimated speech signal, noted S and 1 / n and a second module 22 for modeling the signal of speech at an intermediate rate connected to the extraction system 20 data via a circuit 27 conditional switching on actual flow criteria allocated to the speech signal and delivering a second signal estimated speech, noted S and 2 / n.
  • the decoding system shown in Figure 12 also includes a third modeling module 23 of the speech signal at maximum rate, this module being connected to the data extraction system 20 via of a conditional switching circuit 28 on criterion of the actual bit rate allocated to speech and delivering a third estimated speech signal S and 3 / n.
  • a summing circuit 24 receives the first, the second and the third estimated speech signal, and delivers at its output a resulting estimated speech signal, noted S and n .
  • an adaptive filtering circuit 25 receives the resulting estimated speech signal S and n and delivering a reconstituted estimated speech signal, denoted S and ' n .
  • a digital-to-analog converter 26 may be provided to receive the reconstructed speech signal and to output an audio-frequency reconstituted speech signal.
  • each of the signal modeling modules of speech at a minimum, intermediate and maximum rate includes a inverse adaptive transformation sub-module, followed by a inverse perceptual weighting filter.
  • the object decoding system of the present invention takes into account the constraints imposed by data transmission at the level of coding system and in particular at the dictionary level adaptive, as well as the contribution of past excitement.
  • the speech signal modeling circuit at minimum flow 21 is identical to that described relatively to circuit 17 of the coding system according to the invention at from an inverse adaptive transformation module similar to module 170 described in relation to the figure 11.
  • an inverse adaptive transformation module similar to module 170 described in relation to the figure 11.
  • FIG. 13b an advantageous embodiment of this is shown in FIG. 13b. It is indicated that the embodiment represented in FIG. 13b corresponds to a reverse Householder type transform using elements identical to the Householder transform represented in FIG. 7. It is simply indicated that for a perceptual signal delivered by the long-term prediction circuit 13, this signal being denoted P and 1 entering a similar module 140, the signals entering the module 1402, respectively at the level of the multipliers associated with each register, are inverted. The resulting signal delivered by the summator corresponding to the summator 171 of FIG. 11 is filtered by a filter of inverse transfer function of the transfer function of the perceptual weighting matrix and corresponding to the filter 172 of the same FIG. 11.
  • the speech signal modeling modules at intermediate flow or at maximum flow, module 22 or 23, are shown in Figures 14a and 14b.
  • the gain vectors modeled G and 2 , G and 3 are added, as shown in FIG. 14b, by a summator 220, subjected to the process of inverse adaptive transformation in a module 221 identical to the module 210 of FIG. 13a, then filtered by the inverse weighting filter W -1 (z) previously mentioned, this filter being designated by 222, the filtering starting from zero initial conditions, which makes it possible to perform an operation equivalent to multiplication by the inverse matrix W -1 , in order to obtain a progressive modeling of the synthesis signal S and n .
  • FIG. 14b the presence of switching devices, which are none other than the switching devices 24 and 28 shown in FIG. 12, which are controlled as a function of the actual bit rate of the data transmitted.
  • This adaptive filter makes it possible to improve the perceptual quality of the synthesis signal S and n obtained following the summation by the summator 24.
  • a filter comprises for example a long-term post-filtering module denoted 250, followed by a short-term post-filtering module and an energy control module 252, which is controlled by a module 253 for calculating the scale factor.
  • the adaptive filter 25 delivers the filtered signal S and ' n , this signal corresponding to the signal in which the quantization noise introduced by the coder on the synthesized speech signal has been filtered in the places of the spectrum where this is possible.
  • FIG. 15 corresponds to the publications of JHChen and A.Gersho, "Real Time Vector APC Speech Coding at 4800 Bps with Adaptative Postfiltering", ICASSP 87, Vol.3, pp 2185-2188.
  • the coding system object of the invention allows wideband coding at speech / data rates of 32/0 kbit / s, 24/8 kbit / s and 16/16 kbit / s.

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Claims (10)

  1. System zur prädiktiven Kodierung eines digitalen Signals in ein digitales Signal mit eingebetteten Kodes, welches durch adaptive Transformation mit eingebetteten Kodes kodiert ist, wobei das kodierte digitale Signal aus einem kodierten Sprachsignal und gegebenenfalls aus einem Hilfsdatensignal gebildet ist, welches in das kodierte Sprachsignal nach Kodierung dieses letztgenannten eingefügt wird, wobei das System einen Wahrnehmungsbewertungsfilter (11) enthält, welcher durch eine die Erzeugung eines Wahrnehmungssignals
    Figure 00380001
    erlaubende Kurzzeit-Prädiktionsschleife gesteuert wird, und einen ein geschätztes Wahrnehmungssignal P and 1 / n liefernden Langzeit-Prädiktionskreis enthält, wobei dieser Langzeit-Prädiktionskreis eine Langzeit-Prädiktionsschleife bildet, die es erlaubt, vom Wahrnehmungssignal und vom geschätzten vergangenen Anregungssignal ausgehend ein modelliertes Wahrnehmungsanregungssignal zu liefem, und Mittel zur adaptiven Transformation und Quantisierung enthält, die es erlauben, vom Wahmehmungsanregungssignal ausgehend das kodierte Sprachsignal zu erzeugen, dadurch gekennzeichnet, daß der Wahrnehmungsbewertungsfilter aus einem Kurzzeit-Prädiktionsfilter für das zu kodierende Sprachsignal besteht, um eine Frequenzverteilung des Quantisierungsrauschens zu bewirken, daß es Mittel (12) zur Subtraktion des Beitrags des vergangenen Anregungssignals P and 0 / n vom Wahrnehmungssignal umfaßt, um ein aktualisiertes Wahrnehmungssignal Pn zu liefern, daß der Langzeit-Prädiktionskreis als geschlossene Schleife ausgehend von einem Verzeichnis ausgebildet ist, welches durch die der geringsten Rate entsprechende modellierte vergangene Anregung aktualisiert wird, was es erlaubt, eine optimale Wellenform und eine geschätzte mit dieser verbundene Verstärkung zu liefern, welche wesentlich sind für das geschätzte Wahrnehmungssignal, und daß die Transformationsmittel durch ein Modul zur orthonormierten Transformation gebildet sind, das ein Modul zur adaptiven orthogonalen Transformation und ein Modul zum progressiven Modellieren mittels orthogonaler Vektoren umfaßt, wobei diese Mittel zum progressiven Modellieren und der Langzeit-Prädiktionskreis es erlauben, für das kodierte Sprachsignal repräsentative Indizes zu liefern, wobei das System ferner Mittel (19) zum Einfügen angehängter Hilfsdaten in den Übertragungskanal umfaßt.
  2. Kodiersystem nach Anspruch 1, dadurch gekennzeichnet, daß das Modul zur adaptiven orthogonalen Transformation umfaßt:
    einen Filter, der eine Linearkombination der Basisvektoren bewirkt, die ausgehend von einer Zerlegung der darstellenden Matrix des Wahrnehmungsbewertungsfilters in singuläre Werte erhalten werden.
  3. Kodiersystem nach Anspruch 2, dadurch gekennzeichnet, daß der Filter für jede darstellende Matrix W des Wahrnehmungsbewertungsfilters umfaßt:
    ein erstes Matrixmodul U = (U1,...,UN) und
    ein zweites Matrixmodul V = (V1,...,VN), wobei das erste und das zweite Matrixmodul die Relation UTWV = D erfüllen, worin UT das transponierte Matrixmodul des Moduls U bezeichnet, und worin D ein diagonales Matrixmodul ist, dessen Koeffizienten die singulären Werte bilden, wobei Ui und Vj den i-ten singulären linken Vektor beziehungsweise den j-ten singulären rechten Vektor bezeichnen, wobei die singulären rechten Vektoren {Vj} eine Orthonormalbasis bilden, was es erlaubt, den Vorgang der Filterung mittels Faltungsprodukt durch einen Vorgang der Filterung mittels einer Linearkombination zu transformieren.
  4. Kodiersystem nach Anspruch 1, dadurch gekennzeichnet, daß das Modul zur orthonormierten Transformation aufgebaut ist aus:
    einem zur Initialisierung durch Ziehung einer gaußschen Zufallsvariablen gebildeten Submodul zur stochastischen Transformation,
    einem Modul zur globalen Mittelung über eine Mehrzahl von Vektoren, die aus einem prädiktiven Transformationskodierer stammen,
    einem Modul zur Neuanordnung,
    einem Modul zur Behandlung nach Gram-Schmidt, wobei eine Reiteration der Behandlungen durch die vorhergehenden Module es erlaubt, eine orthonormierte, außerhalb der Reihe durchgeführte, durch Erlernen gebildete Transformation zu erhatten,
    aus einem Speicher vom Typ Lesespeicher, der es erlaubt, die orthonormierte Transformation in Gestalt transformierter Vektoren zu speichern.
  5. Kodiersystem nach Anspruch 4, dadurch gekennzeichnet, daß die Transformation durch orthonormierte Wellenformen gebildet ist, deren Frequenzspektren Bandpässe und relativ zueinander geordnet sind, wobei die erste Wellenforrn aus den orthonormierten, relativ zueinander geordneten Wellenformen gleich der optimalen normierten Wellenform ist, die aus dem adaptiven Verzeichnis stammt, und daß die erste Komponente der geschätzten Verstärkung gleich der normierten Langzeitprädiktions-Verstärkung ist.
  6. Kodiersystem nach Anspruch 2 und 5, dadurch gekennzeichnet, daß das adaptive Transformationsmodul umfaßt:
    ein Householder-Transformationsmodul, welches das aus der optimalen Wellenform und der geschätzten Verstärkung gebildete geschätzte Wahrnehmungssignal p and 1 / 1 und das Wahrnehmungssignal empfängt, um ein transformiertes Wahrnehmungssignal P" in Gestalt eines Vektors des transformierten Wahrnehmungssignals mit Komponenten P"k zu erzeugen,
    eine Mehrzahl von N Registern zur Speicherung der orthonormierten Wellenformen, wobei die Mehrzahl der Register den Lesespeicher bildet, jedes Register vom Rang r N Speicherzellen umfaßt, und eine Komponente vom Rang k jedes Vektors in einer Zelle entsprechenden Rangs gespeichert ist,
    eine die Mehrzahl von Speicherregistern bildende Mehrzahl von N jedem Register zugeordneten Multiplikatorkreisen, wobei jeder Multiplikatorkreis vom Rang k einerseits die Komponente vom Rang k des gespeicherten Vektors und andererseits die Komponente P"k des Vektors des transformierten Wahrnehmungssignals vom Rang k empfängt, und das Produkt P"k·fk orth(k) der Komponenten des Vektors des transformierten Wahrnehmungssignals liefert,
    eine Mehrzahl von N-1 jedem Register vom Rang r zugeordneten Summierkreisen, wobei jeder Summierkreis vom Rang k das durch den Multiplikatorkreis des vorhergehenden Rangs gelieferte Produkt des vorhergehenden Rangs k-1 und das durch den Multiplikatorkreis des vorhergehenden Rangs gelieferte Produkt des entsprechenden Rangs k und das durch den Multiplikatorkreis vom gleichen Rang k gelieferte Produkt des entsprechenden Rangs k empfängt, wobei der Summierkreis des höchsten Rangs, N-1, eine Komponente g(r) der als Verstärkungsvektor G ausgedrückten geschätzten Verstärkung liefert.
  7. System nach Anspruch 1, dadurch gekennzeichnet, daß das Modul zum progressiven Modellieren mittels orthogonaler Vektoren umfaßt:
    ein Modul zur Normierung des Verstärkungsvektors, um mittels Vergleichs des normierten Werts des Verstärkungsvektors G hinsichtlich eines Schwellenwerts einen normierten Verstärkungsvektor Gk zu erzeugen, wobei das Modul zur Normierung es erlaubt, als Funktion des Grads des Modellierens ferner ein Signal von der Länge des normierten Verstärkungsvektors Gk in Richtung des Dekodiersystems zu erzeugen,
    eine Stufe zum progressiven Modellieren mittels orthogonaler Vektoren, die genau gesagt den normierten Vektor Gk empfängt und die repräsentativen Indizes des kodierten Sprachsignals liefert, wobei die Indizes für die ausgewählten Vektoren und ihre zugeordneten Verstärkungen repräsentativ sind, wobei die Übertragung der durch die Indizes gebildeten Hilfsdaten durch Auslöschung der Teile des Rasters bewirkt wird, die den Indizes und Bereichsnummern zum Bilden des Hilfsdatensignals zugewiesen sind.
  8. System zur prädiktiven Dekodierung mittels adaptiver Transformation eines kodierten digitalen Signals mit eingebetteten Kodes, wobei das kodierte digitale Signal aus einem kodierten Sprachsignal und gegebenenfalls aus einem Hilfsdatensignal besteht, welches in das kodierte Sprachsignal nach Kodierung dieses letztgenannten eingefügt wird, dadurch gekennzeichnet, daß es umfaßt:
    Mittel zum Auslesen des Datensignals, die einerseits das Auslesen der Daten in Hinsicht auf eine Hilfsnutzung und andererseits die Übertragung der repräsentativen Indizes des kodierten Sprachsignals erlauben,
    Mittel zum Modellieren des Sprachsignals bei minimaler Rate,
    Mittel zum Modellieren des Sprachsignals bei wenigstens einer Rate, die höher als die minimale Rate ist.
  9. Dekodiersystem nach Anspruch 8, dadurch gekennzeichnet, daß dieser Dekodierer außer dem System zum Datenauslesen umfaßt
    ein erstes Modul zum Modellieren des Sprachsignals bei minimaler Rate, welches direkt das kodierte Signal empfängt und ein erstes geschätzes Sprachsignal S and 1 / n liefert,
    ein zweites Modul zum Modellieren des Sprachsignals bei einer mittleren Rate, welches mit dem System zum Datenauslesen mit Hilfe von Mitteln zur bedingten Kommutation nach dem Kriterium des Werts der Indizes verbunden ist und ein zweites geschätztes Sprachsignal S and 2 / n liefert,
    ein drittes Modul zum Modellieren des Sprachsignals bei einer maximalen Rate, welches mit dem System zum Datenauslesen mit Hilfe von Mitteln zur bedingten Kommutation nach dem Kriterium des Werts der Indizes verbunden ist und ein drittes geschätztes Sprachsignal S and 3 / n liefert,
    einen Summierkreis, der an seinen Summiereingängen das erste, das zweite beziehungsweise das dritte geschätzte Sprachsignal empfängt und an seinem Ausgang ein resultierendes geschätztes Sprachsignal liefert, und mit dem Ausgang des Summierkreises in Kaskade verbunden
    einen adaptiven Filterkreis, der das resultierende geschätzte Sprachsignal empfängt und ein wiederhergestelltes geschätztes Sprachsignal liefert, und einen Digital/Analog-Konverter, der das wiederhergestellte geschätzte Sprachsignal empfängt und ein wiederhergestelltes Audiofrequenz-Sprachsignal liefert.
  10. Dekodiersystem nach Anspruch 9, dadurch gekennzeichnet, daß jedes der Module zum Modellieren des Sprachsignals bei minimaler, mittlerer oder maximaler Rate ein Submodul zur inversen adaptiven Transformation umfaßt, auf das ein inverser Wahrnehmungsbewertungsfilter folgt.
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