EP0135512B1 - Procede et dispositif ameliores de determination des coefficients de codage lineaire par prediction - Google Patents

Procede et dispositif ameliores de determination des coefficients de codage lineaire par prediction Download PDF

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EP0135512B1
EP0135512B1 EP84900575A EP84900575A EP0135512B1 EP 0135512 B1 EP0135512 B1 EP 0135512B1 EP 84900575 A EP84900575 A EP 84900575A EP 84900575 A EP84900575 A EP 84900575A EP 0135512 B1 EP0135512 B1 EP 0135512B1
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coefficients
lattice
ram
matrices
correlation
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EP0135512A4 (fr
EP0135512A1 (fr
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Ira Alan Gerson
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Motorola Solutions Inc
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Motorola Inc
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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
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/08—Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters

Definitions

  • This invention relates to linear predictive coding.
  • it relates to an improved method and means of determining coefficients for linear predictive coding.
  • Linear predictive coding is a method of analysing a speech signal and characterising that signal in terms of coefficients which can be encoded, broadcast, received, and decoded to recover a close approximation to the original signal.
  • the existence of redundancies in speech makes it possible to use encoded descriptions of the speech that can be carried in a communication channel having a bandwidth that is less than the bandwidth of the speech. This is in distinct contrast to many well-known forms of converting speech into digital signals. Most of these methods require a bandwidth that is greater than the bandwidth of the speech.
  • Linear predictive coding (LPC) of speech begins conceptually with a model of the human speech-producing system.
  • the model has a source of sound that is analogous to the vocal cords. That source is coupled acoustically to a stacked array of hollow cylindrical tubes that is analogous to the cavities of the throat and mouth in a human speaker.
  • speech is characterised by four types of quantities. The first of these is a measure of whether speech is voiced or unvoiced.
  • a voiced signal begins with an input from the vocal cords, while an unvoiced signal is produced by the action of the rest of the vocal tract on moving air alone: This produces the differences in sound between "s" which is unvoiced and its voiced equivalent "z", for example.
  • a second characteristic of the sound is the pitch, the fundamental frequency produced by the vocal cords in making a voiced sound.
  • a third characteristic is the energy.
  • the effect of the throat and mouth on either voiced or unvoiced sound is summarized by obtaining some measure of the transfer function of the vocal tract. Such a measure might be the reflection coefficients of the structure, the poles of the transfer function of the structure, the logarithmic area ratios (LAR's) of the structure, or any of several other well-known functions of such resonances.
  • various mathematical transforms of these functions may have utility for particular purposes. The functions are interrelated so that any one set can be determined from a knowledge of any other set.
  • the present invention concerns an improved method and means of determining quantities corresponding to reflection coefficients.
  • the reflection coefficients are coefficients with a specific all pole filter structure known as a lattice filter.
  • the reflection coefficients discussed here are the electrical analog of .acoustical reflection coefficients. While the reflection coefficient is in general a complex quantity in which .the imaginary portion is a measure of loss at a discontinuity, when determining the reflection coefficients that characterize a human vocal tract, the portions of reflection coefficients associated with loss are small enough compared to the real part that denotes lossless reflection that it is adequate to use a lossless model in which the reflection coefficients are real.
  • One way of obtaining the reflection coefficients that characterise a sample of a signal such as speech is to determine the characteristics of an inverse lattice filter that will reproduce the signal when excited by an impulse. Examples of several approaches of performing such an analysis are given in an article entitled “Stable and Efficient Lattice Methods for Linear Prediction” by Makhoul in “IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-25, No. 5, October, 1977 pages 423 ⁇ 428. This article points out that an analysis based on the all-pole lattice filter is stable without windowing but at a computational cost that is several times the cost of the auto-correlation and covariance methods of calculation.
  • Makhoul refers to as the computational costs of computer analysis are proportional to the equipment costs of realizing a circuit for comparable analysis on a semiconductor chip.
  • Two examples of methods of computing partial correlation coefficients for predictive coding are in papers by F. Itakura et al in IEEE Transactions on Communications, August 1972, pages 792-797 and by J. Le Roux and C. Gueguen in IEEE Transactions on Acoustics, Speech and Signal Processing, June 1977, pages 257-259.
  • Fig. 1 is an overall block diagram of a circuit for the practice of the invention.
  • an electrical analog signal that is to be analysed is applied at terminal 10. That signal will typically be an electrical analog of a voice signal, although it may be any electrical signal that exhibits redundancies analogous to those of speech. Examples of such other signals include video scans and seismic analysis records.
  • the signal at terminal 10 is applied to a filter 12, if necessary, to limit its bandwidth. If the bandwidth is already adequate, filter 12 may comprise a direct wire connection, or filter 12 may combine a bandpass filter with any of a number of systems of pre-emphasis that are commonly used in radio broadcasting.
  • ADC analog-to-digital converter
  • the digital output of ADC 14 will be separated into frames of a convenient length, of the order of tens of milliseconds. That function is here indicated as being performed by framer 16 as a means of insuring that the digital input to correlator 18 establishes correlation among samples in the same frame.
  • the function of framer 16 could also be combined into correlator 18 or ADC 14. It should be noted that the ADC 14 is not necessary if the signal is already in digital form.
  • the correlation parameters are obtained in the circuit of Fig. 1 because that circuit will be used to determine reflection coefficients.
  • the output of correlator 18 is applied, if necessary, to a normalizer 20 to normalize output values to a common level.
  • the output of normalizer 20 is taken to random-access memory (RAM) 22 where it is stored in an address that is directed by a signal from multiplexer 23.
  • Normalizer 20 also generates a signal indicating the completion of the operation of correlator 18 for one frame. That signal is taken as an input to sequencer 24. Signals from sequencer 24 are coupled out to control an address calculator 26, an update calculator 28, a reflection coefficient calculator 30, multiplexer 23, and multiplexer 32.
  • Normalizer 20 determines the appropriate addresses in RAM 22 for storing the coefficients of a correlation matrix so that their serial readout will be in a desired order.
  • the output of normalizer 20 is applied to multiplexer 23 to apply first an initial condition to RAM 22 that is determined from normalizer 20, and then accessed by address instructions from address calculator 26.
  • the output of RAM 22 is applied through multiplexer 32 to RAM 34.
  • Multiplexer 32 selects as an input to RAM 34 either the output of RAM 22 or the output of update calculator 28.
  • Multiplexer 32 is controlled by a signal from sequencer 24.
  • the location of storage elements in RAM 34 is controlled by a signal from address calculator 26.
  • the output of RAM 34 is read into update calculator 28 and reflection coefficient calculator 30.
  • Update calculator 28 also receives as an input the calculated reflection coefficients from reflection coefficient calculator 30, which coefficients are taken as the output of the circuit of Fig. 1.
  • Fig. 2 is a circuit diagram of the address calculator 26 of Fig. 1.
  • Address calculator 26 produces the addresses that direct the retrieval of matrix elements in RAMs 22 and the storage and retrieval of matrix elements in RAM 34.
  • sequencer 24 supplies addresses to read-only memory (ROM) 40.
  • ROM 40 is read out into multiplexers 42 and 44.
  • the output of multiplexer 42 is taken to RAM 46 which is loaded under the control of signals from sequencer 24.
  • the output of RAM 46 is taken directly as the input to RAM 34 of Fig. 1, and it is also applied to latches 48 and 50, as well as the add/subtract unit 42.
  • the output of latch 48 is taken to multiplexer 23 of Fig. 1, and then to RAM 22.
  • the output of latch 50 is multiplexed in multiplexer 44 with the output of ROM 40 under the control of a signal from sequencer 24.
  • the output of multiplexer 44 is applied to add-subtract unit 52 where it is supplied as an input to be either added or subtracted from the output of RAM 46, depending upon a control signal from sequencer 24.
  • the add/subtract unit 52 also has an output connected to sequencer 24 which indicates a zero result from the add/subtract operation.
  • Fig. 3 is a circuit diagram of the update calculator 28 of Fig. 1.
  • data inputs are applied at terminals 60 and 62.
  • the input at terminal 60 is the output of RAM 34 of Fig. 1.
  • Terminal 60 is connected to a shifter 64, the output of which is taken to a RAM 66 and to a multiplexer 68.
  • the output of RAM 66 is taken to. multiplexer 70 which, in turn, is connected to multiplier 72.
  • the output of multiplier 72 is taken as an input to multiplexer 68, as an input to register 74, and as an input to multiplexer 76.
  • the output of register 74 is coupled through multiplexer 78 to supply a second input to multiplier 72.
  • Terminal 62 is connected to reflection-coefficient calculator 30 of Fig. 1 to receive calculated reflection coefficients which are then coupled to register 80 of Fig. 3.
  • the outputs of register 74 and 80 are applied to multiplexer 78 to be selected under the control of a signal from sequencer 24 of Fig. 1.
  • the output of register 80 of Fig. 3 is also taken as an input to multiplexer 70.
  • Multiplexer 76 produces an output that is taken to registers 82 and 84, the outputs of which supply inputs to multiplexer 86.
  • the output of multiplexer 86 is taken as an input to multiplexer 70 and as an input to summer 88.
  • the output of summer 88 is taken as input to multiplexer 76 as a data input to RAM 34 of Fig.
  • OR gate 90 1 and as an input to OR gate 90 of Fig. 3.
  • the output of OR gate 90 is taken to register 92 which supplies an output that is taken both as a second input to OR gate 90 and as an input to priority encoder 94.
  • the output of priority encoder 94 controls register 96 that operates shifter 64.
  • the combination of shifter 64, register 96, OR gate 90, register 92 and priority encoder 94 comprises a normalizer that normalizes the output of RAM 34 of Fig. 1. If it is not desired or is considered unnecessary to normalize coefficients, then terminal 60 can supply an input directly to RAM 66 and the elements just described could be removed from the circuit of Fig. 3.
  • Fig. 4 is a circuit diagram of the reflection coefficient calculator 30 of Fig. 1.
  • terminal 100 receives a signal from RAM 34 that is applied to a subtractor 102 and a multiply-by-two circuit 104.
  • the output of subtractor 102 is connected to register 106, which produces one output that is connected as a input to subtractor 102, and another output that is taken to divider 108.
  • the output of divider 108 may be taken directly as a reflection coefficient.
  • the signal at terminal 112 is a reflection coefficient of the equivalent filter of the original input signal.
  • the reflection coefficients may be used directly as the coefficients in LPC or they may be transformed into a different function, as described earlier. If it is desired to convert reflection coefficients into LAR's, this can be combined readily with the quantization that is performed by quantizer 110.
  • the choice of the particular functions is one of design.
  • Filter 12 has already been described as optional. If the circuit of Fig. 1 is to be applied to speech for use in a typical radio speech bandwidth, then the input signal may need to be subjected to bandpass filtering. It may also be desirable to combine some form of pre-emphasis with filter 12. Filter 12 may also be used to prevent aliasing when the filtered signal is applied to ADC 14.
  • the sample rate produced by ADC 14 is a design parameter. According to the well-known sampling theorem, it will be necessary to sample the input at a rate that is at least twice the frequency of the highest component contained in the input. Thus, sampling rates for speech are typically between 6.4 kHz and 10 kHz.
  • Normalizer 20 of Fig. 1 performs a function that is here indicated separately but that might also be included in correlator 18. Normalizer 20 shifts the elements of the correlation matrix so that the maximum value of any element in the array is between one-half and one in magnitude. Normalizer 20 then truncates the values of the elements thus shifted to a number of bits equal to the word length of the system. The covariance matrix thus has a number of elements equal to (p+1 ) 2 .
  • the covariance matrix is symmetric, it can be described completely by storing the elements of the diagonal and the elements below the diagonal, a total of (p+ 1)(p+2)/2 elements. These elements are stored in RAM 22 in a location that is controlled by address calculator 26. A convenient method of loading RAM 22 is to load the diagonal elements, beginning with the element of highest order and proceeding to the diagonal element of lowest order, and then repeat in sequence down paths in the matrix that are parallel to the main diagonal. It should be noted that the F and B matrices are also symmetric and may be stored in a similar fashion.
  • an operations block 122 directs the determination of correlation coefficients. Such a determination is well known. For discrete or sampled components, it is normally accomplished by a calculation such as that of the following equation: where s(n), OsnsN-1 are samples of the audio signal during a frame and p is the order of the filter.
  • the next step in the flowchart of Fig. 5 is to initialize the matrices of F, B and C as indicated in operations block 126.
  • the quantities F, B and C are intermediate quantities used in the determination of LPC coefficients. Their initial values are determined as follows:
  • Operations box 128 next directs that the value of j equal 1.
  • Operations box 130 then calls for the determination of the quantity k j , the jth reflection coefficient. This is determined as follows:
  • the value of j is increased by 1 in operations block 140, and control returns to operations block 130 to continue the calculations.
  • the preceding description shows a circuit for determining LPC coefficients and a flowchart of the steps performed by that circuit.
  • the flowchart of Fig. 5 is also useful in directing the steps of a program for determining LPC coefficients by the same method using a computer. This has been done, and a computer program for determining LPC coefficients using this method is included as an appendix to this application.
  • the computer program has the particular advantage that, using the method of the present invention, calculations may be performed efficiently in integer arithmetic. This speeds calculations and makes it possible to determine LPC coefficients in real time using the present invention.
  • Fig. 6 is a block diagram of a lattice filter that provides a further explanation of the process by which the circuit of Fig. 1 obtains reflection coefficients.
  • a terminal 150 receives as an input the sampled signal. This signal is applied to an upper leg 152 which will calculate a forward residual, and it is applied to a lower leg 154 which will apply the signal to the first of a sequence of delay elements 156 to calculate backward residual.
  • Fig. 6 comprises a cascade of elements, each of which applies the forward signal to a multiplier 158 and a summer 165. The backward signals are applied to a multiplier 162 and a summer 164. Both multipliers 158 and 162 receives as additional inputs the current reflection coefficient. Thus, the current forward residual is multiplied by the current reflection coefficient and added to the current backward residual in summer 164 to generate as an output the next backward residual.
  • the current backward residual is multiplied in multiplied 162 with the current reflection coefficient and added to the current forward residual in summer 160 to generate the next forward residual.
  • the process just described continues through a number of sections of the lattice filter that is determined by the designer as a number adequate to characterize the particular signal in question. This is typically a number of stages equal to 8, 10 or 12. The last such stage is shown here as receiving a current forward residual signal on terminal 166 and a current backward residual signal on terminal 168.
  • the current forward residual signal is applied to a multiplier 170 and a summer 172, while the current backward residual is delayed in delay element 174, and the delayed signal is applied to multiplier 176 and summer 178. Both multipliers 170 and 176 receive as additional inputs the current reflection coefficient. If the lattice filter has been designed with an adequate number of sections to approximate the input signal sufficiently well, then the output of the final forward residual signal at terminal 180 will be close to zero and so will the final backward residual signal at terminal 182.
  • the result of applying a signal to this circuit of Fig. 6 is the production in forward line 152 of a sequence of elements of a forward residual vector and to produce in line 156 the elements of a backward residual vector. Individual elements are combined to produce an autocorrelation matrix of the forward residual elements, and autocorrelation matrix of the backward residual elements, and a cross-correlation matrix between forward and backward residual elements. These matrices are used as described earlier to calculate the reflection coefficients.
  • Lattice methods of determining coefficients for linear predictive coding have been used in the past.
  • circuits and programs used to determine the lattice coefficients have used intermediate variables that varied in magnitude over a wide range. This required a wide range of quantize values to characterize the intermediate variables, and thus took more computational time.
  • the circuit arrangement and method of the present invention uses only variable and intermediate variables which are bounded in magnitude by unity. This permits operations and calculations to be performed in a fixed-point fractional implementation.
  • the input signal is windowed so that it is stationary in a statistical sense, then it can be shown that the number of computations necessary to determine lattice coefficients is reduced still further.
  • the method and means of the present invention has been used with a frame length of approximately 15 milliseconds to determine 12 lattice coefficients in real time. By completing the calculations for the data of one frame before the end of the next succeeding frame.

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  • Engineering & Computer Science (AREA)
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  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • Physics & Mathematics (AREA)
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  • Acoustics & Sound (AREA)
  • Human Computer Interaction (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
  • Medicines Containing Material From Animals Or Micro-Organisms (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)
  • Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
  • Filters That Use Time-Delay Elements (AREA)
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  • Transmission Systems Not Characterized By The Medium Used For Transmission (AREA)

Claims (8)

1. Procédé de traitement d'un signal électrique mis sous forme numérique qui est représentatif d'une voix ou d'un signal autorégressif analogue afin d'obtenir les coefficients de réseau d'un filtre de réseau inverse qui caractérisé le signal mis sous forme numérique, le procédé comprenant les opérations suivantes:
a. échantillonner le signal mis sous forme numérique; et
b. calculer les coefficients de corrélation des échantillons (124);
caractérisé en ce qu'on sélectionne les échantillons dans des périodes de temps égales à une longueur de bloc prédéterminée et en ce qu'on normalise les coefficients de corrélation à un niveau commun, et où, pour chaque bloc, le procédé comprend en outre les opérations suivantes:
c. initialiser les valeurs emmagasinées de matrices F, B et C à l'aide des coefficients de corrélation normalisés (126), où lesdites matrices indiquent respectivement la corrélation (F) des résidus avant, la corrélation (B) des résidus arrière et la corrélation croisé (C) des résidus avant et arrière;
d. calculer une valeur des coefficients de réseau à partir des valeurs emmagasinées des matrices F, B et C, la valeur des coefficients de réseau fournissant une indication de la corrélation entre les résidus avant et arrière;
e. calculer de nouvelles valeurs des matrices F, B et C (138) à partir de la valeur calculée des coefficients de réseau et des valeurs emmagasinées des matrices F, B et C;
f. emmagasiner les nouvelles valeurs des matrices F, B et C;
g. répéter les opérations consistant à calculer une valeur des coefficients de réseau à partir des valeurs emmagasinées des matrices F, B et C, calculer de nouvelles valeurs des matrices F, B et C et emmagasiner les nouvelles valeurs des matrices F, B et C pour un nombre prédéterminé de calculs répétés, le nombre des répétitions indiquant l'ordre du filtre de réseau, jusqu'à ce que la valeur des coefficients de réseau ait atteint une précision prédéterminée; et
h. délivrer la dernière valeur calculée des coefficients de réseau pour le bloc considéré.
2. Procédé selon la revendication 1, où l'opération de calcul des coefficients de corrélation comprend l'opération consistant à obtenir une somme de produits de signaux échantillonnés selon la relation:
Figure imgb0023
où s(n), pour 0<n<n-1, sont des échantillons du signal mis sous forme numérique pendant un bloc, (i,k) sont les coordonnées de la matrice, N est le nombre total d'échantillons, et p est l'ordre du filtre.
3. Procédé selon la revendication 1, où ladite opération d'initialisation des valeurs emmagasinées des matrices F, B et C comprend l'opération consistant à déterminer les valeurs initiales selon les relations:
Figure imgb0024
pour:
Figure imgb0025
où s(n), avec 0<n<N-1, sont les échantillons du signal mis sous forme numérique pendant un bloc, (i,k) sont les coordonnées de la matrice, N est le nombre total d'échantillons, et p est l'ordre du filtre.
4. Procédé selon la revendication 1, où on calcule lesdits coefficients de réseau selon la relation:
Figure imgb0026
où kj est la jème valeur du coefficient de réseau k.
5. Procédé selon la revendication 1, où on calcule les nouvelles valeurs de la matrice F selon la relation:
Figure imgb0027
pour 0≤i≤k≤(P-j-1
6. Procédé selon la revendication 1, où on calcule les nouvelles valeurs de la matrice B selon la relation:
Figure imgb0028
pour 0≤i≤k≤(P-j-1).
7. Procédé selon la revendication 1, où on calcule les nouvelles valeur de la matrice C selon la relation:
Figure imgb0029
pour 0<i, k≤(p-j-1).
8. Appareil permettant de traiter un signal électrique analogique selon le procédé de la revendication 1 afin d'obtenir les coefficients de réseau d'un filtre de réseau inverse qui caractérisent le signal électrique analogique, l'appareil comprenant:
a. un filtre (12) servant à recevoir le signal électrique analogique et à produire un signal électrique analogique filtré;
b. un convertisseur analogique-numérique (14) connecté au filtre afin de produire un signal numérique;
c. un corrélateur (18) connecté au convertisseur analogique-numérique afin de produire des coefficients de corrélation des signaux numériques;
l'appareil étant caractérisé en ce qu'il comprend en outre:
d. un délimiteur de bloc (16) connecté au corrélateur afin de sélectionner des intervalles de temps d'une étendue prédéterminée de façon que des coefficients de corrélation soient produits pendant chaque intervalle délimité;
e. un normalisateur (20) connecté au corrélateur afin de normaliser les coefficients de corrélation du corrélateur;
f. une première RAM (22) connectée au normalisateur afin de recevoir et d'emmagasiner des coefficients de corrélation normalisés;
g. un dispositif de commande de séquence (24) qui produit plusieurs signaux de commande;
h. un calculateur d'adresse (26) connecté au dispositif de commande de séquence et à la première RAM afin de désigner les adresses auxquelles les coefficients de corrélation normalisés sont emmagasinés dans la première RAM;
i. un multiplexeur (32) connecté à la première RAM et recevant en entrée des données de la première RAM;
j. une deuxième RAM (34) connectée au multiplexeur et au calculateur d'adresse, la deuxième RAM recevant son signal d'entrée de la part du multiplexeur et emmagasinant ce signal d'entrée en un emplacement désigné par le calculateur d'adresse;
k. un calculateur de coefficients de réseau (30) connecté à la deuxième RAM et au dispositif de commande de séquence, le calculateur de coefficients de réseau recevant des données de la part de la deuxième RAM et calculant les coefficients de réseau sous commande du dispositif de commande de séquence; et
I. un circuit de remise à jour (28) connecté au calculateur de coefficients de réseau, à la deuxième RAM et au dispositif de commande de séquence afin de calculer les coefficients de réseau courants sous commande du dispositif de commande de séquence et de produire un signal de sortie qui est pris comme signal d'entrée du multiplexeur.
EP84900575A 1983-01-03 1983-12-22 Procede et dispositif ameliores de determination des coefficients de codage lineaire par prediction Expired EP0135512B1 (fr)

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GB1318985A (en) * 1970-02-07 1973-05-31 Nippon Telegraph & Telephone Audio response apparatus
GB2026289B (en) * 1978-04-12 1982-04-21 Secr Defence Self-adaptive linear prediction filters
JPS597120B2 (ja) * 1978-11-24 1984-02-16 日本電気株式会社 音声分析装置
JPS55164700U (fr) * 1979-05-14 1980-11-26
JPS5853358B2 (ja) * 1980-03-31 1983-11-29 株式会社東芝 音声分析装置
US4378469A (en) * 1981-05-26 1983-03-29 Motorola Inc. Human voice analyzing apparatus

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IEEE Transactions on Acoustics and Signal Processing, Vol. ASSP-25, No. 5, Oct. 1977, pages 423-428 *

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EP0135512A1 (fr) 1985-04-03
FI843448A (fi) 1984-09-03

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