EP0608174A1 - 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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EP0608174A1
EP0608174A1 EP94400109A EP94400109A EP0608174A1 EP 0608174 A1 EP0608174 A1 EP 0608174A1 EP 94400109 A EP94400109 A EP 94400109A EP 94400109 A EP94400109 A EP 94400109A EP 0608174 A1 EP0608174 A1 EP 0608174A1
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signal
module
speech signal
perceptual
transform
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EP0608174B1 (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 predictive coding-decoding system for a digital speech signal by adaptive transform with nested codes.
  • this type of coder being represented in FIG. 1, it is sought to construct a synthetic signal Sn as close as possible to the digital speech signal to be coded Sn, resemblance in the sense of a perceptual criterion.
  • the digital signal to be coded Sn originating from an analog source speech signal, is subjected to a short-term prediction process, LPC analysis, the prediction coefficients being obtained by prediction of the speech signal on windows comprising M samples.
  • the digital speech signal to be coded Sn is filtered by means of a perceptual weighting filter W (z) deduced from the aforementioned prediction coefficients, to obtain the perceptual signal 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 n , which is subtracted from the signal perceptual pn so as to obtain the signal p'n in the form of a vector P'e RN.
  • 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 n .
  • the Karhunen-Loeve transform obtained from the eigenvectors of the autocorrelation matrix where is the number of vectors contained in the learning corpus, allows to maximize the expression where K is an integer, KZ N.
  • K is an integer
  • KZ 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 1-61 - 1-64.
  • sub-optimal transforms such as the Fast Fourier transform (FFT), the discrete cosine transform (TCD) the discrete transform of Hadamard (DHT) or Walsh Hadamard (DWHT) for example.
  • FFT Fast Fourier transform
  • TCD discrete cosine transform
  • 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:
  • Coders with nested codes currently known make it possible to transmit parvol data of binary elements normally allocated to speech on the transmission channel, and this, in a manner transparent to the coder, which codes the speech signal at the maximum bit rate.
  • a 64 kbit / s encoder with scaled quantizer with nested codes was standardized in 1986 by standard G 722 established by the CCITT.
  • This coder operating in the field of wideband speech (audio signal with a bandwidth of 50 Hz to 7 kHz, sampled at 16 kHz), is based on a coding in two sub-bands each containing a Pulse Modulation coder and Adaptive Differential Coding (MICDA coding).
  • MICDA coding Adaptive Differential Coding
  • This coding technique allows broadband speech signals and data, if necessary, to be transmitted on a 64 kbit / s channel, at three different bit rates 64-56-48 kbit / s and 0-8-16 kbit / s for data.
  • the aforementioned prior art transform predictive coders do not make it possible to transmit data and therefore cannot fulfill the function of nested code coders.
  • the nested code coders of the prior art do not use the orthonormal transform technique, which does not make it possible to tend towards or to achieve optimal transform coding.
  • the object of the present invention is to remedy the aforementioned drawback by implementing a predictive coding-decoding system for a digital speech signal by adaptive transform with nested codes.
  • Another object of the present invention is the implementation of a predictive coding-decoding system for a digital speech and data signal allowing transmission at reduced and flexible rates.
  • the system for predictive coding of a digital signal into a digital code nested code signal in which the coded digital signal consists of a coded speech signal and, where appropriate, of an auxiliary data signal inserted into the coded speech signal after coding of the latter, object of the present invention, comprises a perceptual weighting filter controlled by a short-term prediction loop making it possible to generate a perceptual signal and a long-term prediction circuit delivering an estimated perceptual signal, this circuit long-term prediction signal forming a long-term prediction loop making it possible to deliver, from the perceptual signal and the estimated past excitation signal, a modeled perceptual excitation signal and adaptive transform and quantization circuits making it possible to of the perceptual excitation signal to generate the coded speech signal.
  • the perceptual weighting filter consists of a short-term prediction filter of the speech signal to be coded, so as to achieve a frequency distribution of the quantization noise and in that it comprises a circuit for subtracting the contribution of the past excitation signal from the perceptual signal to deliver an updated perceptual signal, the long-term prediction circuit being formed, in closed loop, from a dictionary updated by the past excitation modeled corresponding to the lowest bit rate allowing the delivery of an optimal waveform and a gain associated with it, constituting the estimated perceptual signal.
  • the transform circuit is formed by an orthonormal transform module comprising an adaptive orthogonal transformation module and a progressive modeling module by orthogonal vectors. The progressive modeling module and the long-term prediction circuit make it possible to deliver indexes representative of the coded speech signal.
  • An auxiliary data insertion circuit is coupled to the transmission channel.
  • the system for adaptive transform predictive decoding of a nested coded digital signal in which the coded digital signal consists of a coded digital signal and, where appropriate, of an auxiliary data signal inserted into the coded speech signal after coding of the latter, is remarkable in that it comprises a circuit for extracting the data signal allowing, on the one hand, the extraction of the data for an auxiliary use and, on the other hand, the transmission of 'indexes representative of the coded speech signal. It further comprises a circuit for modeling the speech signal at the minimum bit rate and a circuit for modeling the speech signal at at least one bit rate greater than the minimum bit rate.
  • the predictive coding-decoding system of a digital speech signal by adaptive transform with nested codes object of the present invention finds application, in general, to the transmission of speech and data at flexible rates, and, more particularly , audiovisual conference protocols, videophone, loudspeaker telephony, storage and transport of digital audio signals over long distance links, transmission with mobiles and channel concentrating systems.
  • the digital signal coded by the implementation of the coding system which is the subject of the present invention consists of a coded speech signal and, where appropriate, by an auxiliary data signal inserted into the coded speech signal. , after coding of this digital speech signal.
  • the coding system which is the subject of the present invention may comprise, from a transducer delivering the analog speech signal, an analog-digital converter and an input storage circuit or input buffer making it possible to deliver the digital signal to code Sn.
  • the coding system which is the subject 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, denoted P.
  • the long-term prediction circuit 13 forms a long-term prediction loop making it possible to deliver, from the perceptual signal and from the estimated past excitation signal, denoted p n o , a modeled perceptual excitation signal.
  • 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 figure 2, a circuit 120 for subtracting the contribution of the past excitation signal P ⁇ 0 n from the perceptual signal to deliver a refreshed perceptual signal, this refreshed perceptual signal being noted P n .
  • the long-term prediction circuit 13 is formed in a closed loop from a dictionary updated by the past excitation modeled corresponding to the lowest bit rate, this dictionary to deliver an optimal waveform and an estimated gain associated with it.
  • the modeled past excitation corresponding to the lowest flow rate is noted r ⁇ 1 n . It is further indicated that the optimal waveform and the estimated gain associated with it constitute the estimated perceptual signal Pn delivered by the long-term prediction circuit 13.
  • the transform module circuit is formed by an orthonormal transform module 14, comprising an adaptive orthogonal transformation module proper and a progressive modeling module using orthogonal vectors, noted 16.
  • the progressive modeling module 16 and the long-term prediction circuit 13 make it possible to deliver indices representative of the coded speech signal, these indices being denoted i (0 ), j (0) respectively i (1), j (1) with 1 e [1, L] in Figure 2.
  • the coding system further comprises a circuit 19 for inserting auxiliary data coupled to the transmission channel, noted 18.
  • the synthetic signal S n is of course the signal reconstituted 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 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 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 n .
  • the second operation consists in removing the contribution of the past excitation, or estimated past excitation signal, noted P ⁇ n 0 from the 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 ⁇ 1 n is the past excitation modeled corresponding to the lowest flow rate, as well as will be described later in the description.
  • the operating mode of the long-term prediction circuit 13 in closed loop is then as follows. This circuit makes it possible to take into account the periodicity of the residue for the voiced sounds, this long-term prediction being carried out all the sub-windows of N samples, as will be described in connection with FIG. 3.
  • the closed-loop long-term 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 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 o and q.
  • the waveform of index i. noted from the adaptive dictionary is filtered by a filter 131 and corresponds to the excitation modeled at the lowest rate r delayed by q samples by the aforementioned filter.
  • the optimal waveform fo 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 f j (0) n, to perform 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 the filtered adaptive dictionary 133, that is to say from the filtering result of the waveform of index j C; ,, or fn, and of the associated quantized gain g (0) , the long-term prediction excitation modeled and filtered perceptually noted P n 1 .
  • a module 136 makes it possible to calculate the Euclidean norm 1 in 12.
  • 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 the modeling of the long-term prediction signal are then the index j (0) of the optimal waveform f (0) as well as the number i (0 ) of the quantization range of its associated gain g (0) quantized.
  • FIGS. 4a and 4b A more detailed description of the adaptive orthogonal transformation module MT of FIG. 2 will be given in conjunction with FIGS. 4a and 4b.
  • the method used for the construction of this transform corresponds to that proposed by BSAtal and E.Ofer, as mentioned previously 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 impulse 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 coder by transform and in FIG. 4b, the corresponding equivalent diagram in which the matrix or filter of perceptual weighting W, designated by 140, has been highlighted, a inverse perceptual weighting filter 121 having however been inserted between the long-term prediction module 13 and the subtracting circuit 120. It is indicated that the filter 140 achieves a linear combination of the basic vectors obtained from a decomposition into singular values of the representative matrix 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 ⁇ 1 n filtered by a reverse perceptual 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 '.
  • This filtering operation is written: and can be expressed as a linear combination of basic vectors using the decomposition into singular values of the matrix W.
  • Such a decomposition makes it possible to replace the filtering operation by convolution product by a filtering operation by a linear combination.
  • the matrix W is then decomposed into a sum of matrices of rank 1, and verifies the relation:
  • the weighted perceptual signal P 'then breaks down as follows:
  • the weighted perceptual signal modeled P is calculated in the following manner:
  • the short-term analysis filtering circuit 10 being updated on windows of M samples, the decomposition into singular values of the perceptual weighting matrix W is carried out at the same frequency.
  • the orthonormal transform process is constructed by learning.
  • the orthonormal transform module can be formed by a stochastic transform sub-module constructed by drawing a Gaussian random variable for initialization, this sub-module comprising in FIG. 5 the process steps 1000, 1001, 1002 and 1003 and being noted SMTS.
  • Step 1002 can consist in applying the K-average algorithm to the aforementioned vector corpus.
  • the SMTS sub-module is successively followed by a module 1004 for building centers, a module 1005 for building classes and, in order to obtain a vector G whose components are relatively ordered, by a module 1006 for reordering of the transform according to the cardinal of each class.
  • the aforementioned module 1006 is followed by a Gram-Schmidt calculation module, noted 1007a, so as to obtain an orthonormal transform.
  • the aforementioned module 1007a is associated with a module 1007b for calculating the error under the conventional conditions for implementing the Gram-Schmidt processing process.
  • the module 1007a is itself followed by a module 1008 for testing the number of iterations, this in order to allow an orthonormal transform carried out offline by learning to be obtained.
  • the memory 1009 of read-only memory type makes it possible to store the orthonormal transform in the form of a transform vector. It is indicated that the relative ordering of the components of the gain vector G is accentuated by the process of orthogonalisation. When the learning construction process has converged, an orthonormal transform is obtained whose waveforms are gradually correlated with the vector learning corpus delivered by the initial transform step 1001.
  • FIG. 5b represents the ordering of the components of the gain vector G, that is to say of the normalized mean value G for a transform obtained on the one hand by decomposition into singular values of the perceptual weighting matrix W, and on the other hand, by learning.
  • An evaluation of the quality of transformation in terms of energy concentration made it possible to show that, by way of indication, on a corpus of 38,000 perceptual vectors P ′, the transformation gain is 10.35 decibels for the optimal Karhunen- transform. Loeve, and 10.29 decibels for a transform constructed by learning, the latter therefore tending towards the optimal transform in terms of energy concentration.
  • the orthonormal transform F can be obtained according to two different methods.
  • the new dimension of the gain vector G then becomes equal to N-1, which makes it possible to increase the number of binary elements per sample during the vector quantization of the latter and therefore the quality of its modeling.
  • a first solution for calculating the transform F 'can then consist in making a long-term prediction analysis, shifting the transform obtained by learning a notch, placing the long-term predictor in the first position, then applying the Gram-Schmidt algorithm, in order to obtain a new transform F '.
  • a second, more advantageous solution consists in using a transformation making it possible to rotate the orthonormal base, so that the first waveform coincides with the long-term predictor, that is to say: with
  • the transformation used must keep the dot product.
  • FIGS. 6a and 6b A geometric representation of the aforementioned transform is given in FIGS. 6a and 6b.
  • the transformation is applied only to the perceptual signal P, and the modeled perceptual signal P 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 perceptual signal. P to generate a transformed perceptual signal P ".
  • the Householder transformation module 140 comprises a calculation module 1401 of the parameters B and wB as defined previously by the relation 13. It also comprises a module 1402 comprising a multiplier and a subtractor making it possible to carry out the transformation proper according to relation 14. It is indicated that the transformed perceptual signal P "is delivered in the form of vector of perceptual signal transformed of component P" k , with ke [0, N-1].
  • the adaptive transformation module 14 as shown in FIG. 7 also includes a plurality N of registers for memorizing orthonormal waveforms, the current register being denoted r, with re [1, N]. It is indicated that 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 cell of corresponding rank of the current register r considered.
  • the module 14 comprises a plurality of N multiplier circuits associated with each rank register reforming the plurality of the previously mentioned storage registers.
  • each multiplier register of rank 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 multiplier circuit Mrk delivers the product P "kf: 'tt (k) of the components of transformed perceptual signal.
  • each summing circuit of rank k denoted Srk
  • receives the product of prior rank k-1 and the product of corresponding rank k delivered by the circuit.
  • multiplier Mrk of the same rank k The summing circuit of highest rank, SrN-1, then delivers a component g (r) of the estimated gain expressed in the form of gain vector G.
  • 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 modeling module by orthogonal vectors further comprises, in cascade with the module 15 for normalization of the gain vector, a stage 16 of progressive modeling by orthogonal vectors.
  • This modeling stage 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 of the selected vectors and their associated gain.
  • the transmission of the auxiliary data formed by the indexes is carried out by overwriting the parts of the frame allocated to the indices and track numbers to form the auxiliary data signal.
  • the operation of the standardization module 15 is as follows.
  • 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 normalized criterion as a function of the modeling order K is given in FIG. 8a for an orthonormal transform obtained on the one hand by decomposition into singular values of the perceptual weighting matrix W and on the other hand by learning.
  • a particularly advantageous embodiment of the progressive modeling module by orthogonal vectors 16 will now be given in connection with FIG. 8b.
  • the aforementioned module makes it possible in fact to perform a multistage vector quantization.
  • the gain vector G is obtained by linear combination of vectors, noted
  • 8 1 is the gain associated with the optimal vector ⁇ j (1) K from the stochastic dictionary of rank 1, noted 16 1.
  • the vectors selected iteratively are generally not linearly independent and therefore do not form a basis.
  • the subspace generated by the L optimal vectors ⁇ j (L) K is of dimension less than L.
  • the projection of the vector G is represented on the subspace generated by the optimal vectors of rank I, respectively 1-1, this projection being optimal when the aforementioned vectors are orthogonal.
  • FIGS. 10a and 10b Diagrams of the principle of vector quantization by orthogonal progressive modeling are given in FIGS. 10a and 10b depending on whether there are one or more stochastic dictionaries.
  • Q is an orthonormal matrix
  • R is 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 0 (k) relative to the original base.
  • the parameters transmitted by the coding system object of the invention for the modeling of the gain vector G are then the indices j (I) of the selected vectors as well as the numbers i (l) of the ranges of quantification of their associated gains , ⁇ 1 .
  • the data transmission is then done by overwriting the parts of the frame allocated to the indices and track numbers j (I), i (I), for 1 ⁇ [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 ⁇ k ⁇ , it is necessary to code the various aforementioned gains g (0) and ⁇ k ⁇ .
  • a study has shown that the gains relative to the orthogonal base ⁇ j (I) orth (L) ⁇ being decorrelated, these have good properties for their quantification.
  • the gains ⁇ 1 ⁇ 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 which is the subject of the present invention comprises a module for modeling the excitation of the synthesis filter corresponding to the lowest bit rate, 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 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 in closed loop.
  • 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 ⁇ 1 n .
  • 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 (I), for 1 between 0 and L 1 -1 previously described in the description and for I between I 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 minimum flow above.
  • the decoding system comprises, in addition to the data extraction system 20, a first module 21 for modeling the speech signal at the minimum bit rate directly receiving the coded signal and delivering a first estimated speech signal, denoted S 1 n and a second module 22 for modeling the speech signal at an intermediate rate connected to the data extraction system 20 via a switching circuit 27 conditional on the criterion of the actual bit rate allocated to the speech signal and delivering a second estimated speech signal, denoted AS n 2 .
  • the decoding system shown in FIG. 12 also includes a third module 23 for modeling the speech signal at a maximum rate, this module being connected to the data extraction system 20 via a circuit 28 for conditional switching on criterion of the actual bit rate allocated to speech and delivering a third estimated speech signal Sn-In addition, a summing circuit 24 receives the first, second and third estimated speech signal, and delivers at its output a resulting estimated speech signal , noted S n . At the output of the summing circuit 24 are connected in cascade an adaptive filtering circuit 25 receiving the resulting estimated speech signal S n and delivering a reconstituted estimated speech signal, denoted S ' 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 modules for modeling the speech signal at a minimum, intermediate and maximum bit rate includes an inverse adaptive transformation sub-module, followed by an inverse perceptual weighting filter.
  • FIG. 13a The block diagram of the speech signal modeling module at minimum bit rate is given in FIG. 13a.
  • the decoding system object of the present invention takes into account the constraints imposed by the transmission of data at the level of the coding system and in particular at the level of the adaptive dictionary, as well as the contribution of the past excitation.
  • the circuit for modeling the speech signal at minimum bit rate 21 is identical to that described for circuit 17 of the coding system according to the invention from an inverse adaptive transformation module similar to module 170 described in relation to FIG. 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 p1 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.
  • modules for modeling the speech signal at the intermediate rate or at the maximum rate, module 22 or 23, are shown in FIGS. 14a and 14b.
  • the gain vectors modeled G 2 , G 3 are added, as represented 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 - '(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 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 n obtained following the summation by the summator 24.
  • a such a filter includes for example a post-filtering module long-term noted 250, followed by a short-term post-filtering module and an energy control module 252, which is controlled by a scale factor calculation module 253.
  • the adaptive filter 25 delivers the filtered signal ⁇ S'n, this signal corresponding to the signal in which the quantization noise introduced by the encoder 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 etA.Gersho, "Real Time Vector APC Speech Coding at 4800 Bps with Adaptative Postfiltering", ICASSP 87, Vol.3, pp 2185-2188.
  • the coding system which is the subject 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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EP94400109A 1993-01-21 1994-01-18 System zur prädiktiven Kodierung/Dekodierung eines digitalen Sprachsignals mittels einer adaptiven Transformation mit eingebetteten Kodes Expired - Lifetime EP0608174B1 (de)

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FR9300601A FR2700632B1 (fr) 1993-01-21 1993-01-21 Système de codage-décodage prédictif d'un signal numérique de parole par transformée adaptative à codes imbriqués.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0751492A2 (de) * 1995-06-28 1997-01-02 ALCATEL ITALIA S.p.A. Verfahren und Vorrichtung zur Kodierung und Dekodierung eines Sprachsignalmusters
EP0792502A1 (de) * 1995-09-14 1997-09-03 Motorola, Inc. Asymmetrische sprachkompression verwendendes und mit sehr niedriger bitrate arbeitendes sprachnachrichtensystem
US6107430A (en) * 1996-03-14 2000-08-22 The Dow Chemical Company Low application temperature hot melt adhesive comprising ethylene α-olefin

Families Citing this family (48)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5822436A (en) * 1996-04-25 1998-10-13 Digimarc Corporation Photographic products and methods employing embedded information
FR2722631B1 (fr) * 1994-07-13 1996-09-20 France Telecom Etablissement P Procede et systeme de filtrage adaptatif par egalisation aveugle d'un signal telephonique numerique et leurs applications
FR2729245B1 (fr) * 1995-01-06 1997-04-11 Lamblin Claude Procede de codage de parole a prediction lineaire et excitation par codes algebriques
JP3046213B2 (ja) * 1995-02-02 2000-05-29 三菱電機株式会社 サブバンド・オーディオ信号合成装置
MX9708203A (es) * 1996-02-26 1997-12-31 At & T Corp Cuantificacion de señales vocales usando modelos de publico humano en sistemas de codificacion predictivas.
JP3878254B2 (ja) * 1996-06-21 2007-02-07 株式会社リコー 音声圧縮符号化方法および音声圧縮符号化装置
US6038528A (en) * 1996-07-17 2000-03-14 T-Netix, Inc. Robust speech processing with affine transform replicated data
JP3263347B2 (ja) * 1997-09-20 2002-03-04 松下電送システム株式会社 音声符号化装置及び音声符号化におけるピッチ予測方法
JP2000197054A (ja) * 1998-12-24 2000-07-14 Hudson Soft Co Ltd 動画像符号方法及びそのプログラムを記録した記録媒体並びに装置
AU2001249785A1 (en) * 2000-04-03 2001-10-15 Flint Hills Scientific, L.L.C. Method, computer program, and system for automated real-time signal analysis fordetection, quantification, and prediction of signal changes
US6768969B1 (en) * 2000-04-03 2004-07-27 Flint Hills Scientific, L.L.C. Method, computer program, and system for automated real-time signal analysis for detection, quantification, and prediction of signal changes
SE522261C2 (sv) * 2000-05-10 2004-01-27 Global Ip Sound Ab Kodning och avkodning av en digital signal
US6993477B1 (en) * 2000-06-08 2006-01-31 Lucent Technologies Inc. Methods and apparatus for adaptive signal processing involving a Karhunen-Loève basis
US8948059B2 (en) 2000-12-26 2015-02-03 Polycom, Inc. Conference endpoint controlling audio volume of a remote device
US8964604B2 (en) 2000-12-26 2015-02-24 Polycom, Inc. Conference endpoint instructing conference bridge to dial phone number
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US7339605B2 (en) 2004-04-16 2008-03-04 Polycom, Inc. Conference link between a speakerphone and a video conference unit
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US8934382B2 (en) 2001-05-10 2015-01-13 Polycom, Inc. Conference endpoint controlling functions of a remote device
US8976712B2 (en) 2001-05-10 2015-03-10 Polycom, Inc. Speakerphone and conference bridge which request and perform polling operations
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US8947487B2 (en) 2001-12-31 2015-02-03 Polycom, Inc. Method and apparatus for combining speakerphone and video conference unit operations
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US7796565B2 (en) * 2005-06-08 2010-09-14 Polycom, Inc. Mixed voice and spread spectrum data signaling with multiplexing multiple users with CDMA
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US8108438B2 (en) * 2008-02-11 2012-01-31 Nir Asher Sochen Finite harmonic oscillator
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0462559A2 (de) * 1990-06-18 1991-12-27 Fujitsu Limited System zur Sprachcodierung und -decodierung
EP0492459A2 (de) * 1990-12-20 1992-07-01 SIP SOCIETA ITALIANA PER l'ESERCIZIO DELLE TELECOMUNICAZIONI P.A. System für eingebettetes Kodieren von Sprachsignalen

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
NL8802291A (nl) * 1988-09-16 1990-04-17 Koninkl Philips Electronics Nv Inrichting voor het verzenden van datawoorden welke een gedigitaliseerde analoog signaal vertegenwoordigen en een inrichting voor het ontvangen van de verzonden datawoorden.
EP0443548B1 (de) * 1990-02-22 2003-07-23 Nec Corporation Sprachcodierer
US5371853A (en) * 1991-10-28 1994-12-06 University Of Maryland At College Park Method and system for CELP speech coding and codebook for use therewith

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0462559A2 (de) * 1990-06-18 1991-12-27 Fujitsu Limited System zur Sprachcodierung und -decodierung
EP0492459A2 (de) * 1990-12-20 1992-07-01 SIP SOCIETA ITALIANA PER l'ESERCIZIO DELLE TELECOMUNICAZIONI P.A. System für eingebettetes Kodieren von Sprachsignalen

Non-Patent Citations (13)

* Cited by examiner, † Cited by third party
Title
"Embedded CELP Coding For Variable Bit-Rate Between 6.4 and 9.6 kbit/s", ICASSP 91, vol. L, pages 681 - 684
ALAN O.STEINHARDT: "Householder Transforms in Signal Processing", IEEE ASSP MAGAZINE, July 1988 (1988-07-01), pages 4 - 12, XP011437206, DOI: doi:10.1109/53.9259
B.S.: "A Model of LPC Excitation in Terms of Eigenvectors of the Autocorrelation Matrix of the Impulse Response of the LPC Filter", ICASSP 89, vol. L, pages 45 - 48
CHEN, GERSHO: "Real-time vector APC speech coding at 4800 BPS with adaptive postfiltering", INTERNATIONAL CONFERENCE ON ACOUSTICS SPEECH AND SIGNAL PROCESSING, vol. 4, 6 April 1987 (1987-04-06), DALLAS TEXAS, pages 2185 - 2188 *
DYMARSKI ET AL.: "Optimal and sub-optimal algorithms for selecting the excitation in linear predictive coders", INTERNATIONAL CONFERENCE ON ACOUSTICS SPEECH AND SIGNAL PROCESSING, vol. 1, 3 April 1990 (1990-04-03), ALBUQUERQUE NEW MEXICO US, pages 485 - 488 *
E.OFER: "A Unified Framework for LPC Excitation Repre- sentation in Residual Speech Coders", ICASSP 89, vol. L, pages 41 - 44
G.DAVIDSON; A.GERSHO: "Multiple-Stage Vector Excitation Coding of Speech Wave forms", ICASSP 88, vol. L, pages 163 - 166
J.H.CHEN; A.GERSHO: "Real Time Vector APC Speech Coding at 4800 Bps with Adaptative Postfiltering", ICASSP 87, vol. 3, pages 2185 - 2188
M.JOHNSON; T.TANIGUSHI: "Pitch Orthogonal Code-Excited LPC", GLOBECOM 90, vol. 1, pages 542 - 546
MOREAU, DYMARSKI: "Successive orthogonalizations in the multistage CELP coder", INTERNATIONAL CONFERENCE ON ACOUSTICS SPEECH AND SIGNAL PROCESSING, vol. 1, 23 March 1992 (1992-03-23), SAN FRANCISCO CALIFORNIA US, pages 61 - 64, XP010058716, DOI: doi:10.1109/ICASSP.1992.225972 *
N.MOREAU; P.DYMARSKI: "Successive Orthogonalisations in the Multistage CELP Coder", ICASSP 92, vol. L, pages 1 - 61
N.MOREAU; P.DYMARSKI; A.VIGIER: "Optimal and Suboptimal Algorithms for Selecting the Excitation in Linear Predictive Products", PROC. ICASSP 90, pages 485 - 488
R.ROSE; T.BARNWELL: "Design and Performance of an Analysis by Synthesis Class of Predictive Speech Coders", IEEE TRANS. ON ACOUSTIC SPEECH SIGNAL PROCEESSING, September 1990 (1990-09-01)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0751492A2 (de) * 1995-06-28 1997-01-02 ALCATEL ITALIA S.p.A. Verfahren und Vorrichtung zur Kodierung und Dekodierung eines Sprachsignalmusters
EP0751492A3 (de) * 1995-06-28 1998-03-04 ALCATEL ITALIA S.p.A. Verfahren und Vorrichtung zur Kodierung und Dekodierung eines Sprachsignalmusters
US5809456A (en) * 1995-06-28 1998-09-15 Alcatel Italia S.P.A. Voiced speech coding and decoding using phase-adapted single excitation
EP0792502A1 (de) * 1995-09-14 1997-09-03 Motorola, Inc. Asymmetrische sprachkompression verwendendes und mit sehr niedriger bitrate arbeitendes sprachnachrichtensystem
EP0792502A4 (de) * 1995-09-14 1998-12-23 Motorola Inc Asymmetrische sprachkompression verwendendes und mit sehr niedriger bitrate arbeitendes sprachnachrichtensystem
US6107430A (en) * 1996-03-14 2000-08-22 The Dow Chemical Company Low application temperature hot melt adhesive comprising ethylene α-olefin

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US5583963A (en) 1996-12-10
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DE69412294T2 (de) 1999-04-15
FR2700632B1 (fr) 1995-03-24

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