EP0899720B1 - Quantisation des coefficients de prédiction linéaire - Google Patents

Quantisation des coefficients de prédiction linéaire Download PDF

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EP0899720B1
EP0899720B1 EP98306906A EP98306906A EP0899720B1 EP 0899720 B1 EP0899720 B1 EP 0899720B1 EP 98306906 A EP98306906 A EP 98306906A EP 98306906 A EP98306906 A EP 98306906A EP 0899720 B1 EP0899720 B1 EP 0899720B1
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lsf
quantizer
lpc
codebooks
filter
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EP0899720A2 (fr
EP0899720A3 (fr
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Alan V. Mccree
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Texas Instruments Inc
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Texas Instruments 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
    • G10L19/02—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
    • G10L19/032—Quantisation or dequantisation of spectral components
    • G10L19/038—Vector quantisation, e.g. TwinVQ audio
    • 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
    • G10L19/06—Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
    • G10L19/07—Line spectrum pair [LSP] vocoders
    • 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
    • G10L2019/0013—Codebook search algorithms
    • G10L2019/0014—Selection criteria for distances

Definitions

  • This invention relates to switched-predictive vector quantization and more particularly to quantization of LPC coefficients transformed to line spectral frequencies.
  • the MELP coder is based on the traditional LPC vocoder with either a periodic impulse train or white noise exciting a 10th order on all-pole LPC filter.
  • the synthesizer has the added capabilities of mixed pulse and noise excitation periodic or aperiodic pulses, adaptive spectral enhancement and pulse dispersion filter as shown in Fig. 1.
  • Efficient quantization of the LPC coefficients is an important problem in these coders, since maintaining accuracy of the LPC has a significant effect on processed speech quality, but the bit rate of the LPC quantizer must be low in order to keep the overall bit rate of the speech coder small.
  • the MELP coder for the new Federal Standard uses a 25-bit multi-stage vector quantizer (MSVQ) for line spectral frequencies (LSF) . There is a 1 to 1 transformation between the LPC coefficients and LSF coefficients.
  • Quantization is the process of converting input values into discrete values in accordance with some fidelity criterion.
  • a typical example of quantization is the conversion of a continuous amplitude signal into discrete amplitude values. The signal is first sampled, then quantized.
  • a range of expected values of the input signal is divided into a series of subranges. Each subrange has an associated quantization level. A sample value of the input signal that is within a certain subrange is converted to the associated quantizing level. For example, for 8-bit quantization, a sample of the input signal would be converted to one of 256 levels, each level represented by an 8-bit value.
  • Vector quantization is a method of quantization, which is based on the linear and non-linear correlation between samples and the shape of the probability distribution. Essentially, vector quantization is a lookup process, where the lookup table is referred to as a "codebook”. The codebook lists each quantization level, and each level has an associated "code-vector". The vector quantization process compares an input vector to the code-vectors and determines the best code-vector in terms of minimum distortion. Where x is the input vector, the comparison of distortion values may be expressed as: d(x, y (J) ) ⁇ d(x, y (k) ), for all j not equal to k.
  • the codebook is represented by y (j) , where y (j) is the jth code-vector, 0 ⁇ j ⁇ L, and L is the number of levels in the codebook.
  • Multi-stage vector quantization is a type of vector quantization. This process obtains a central quantized vector (the output vector) by adding a number of quantized vectors. The output vector is sometimes referred to as a "reconstructed" vector. Each vector used in the reconstruction is from a different codebook, each codebook corresponding to a "stage" of the quantization process. Each codebook is designed especially for a stage of the search. An input vector is quantized with the first codebook, and the resulting error vector is quantized with the second codebook, etc.
  • S the number of stages
  • y s the codebook for the sth stage.
  • the codebooks may be searched using a sub-optimal tree search algorithm, also known as an M-algorithm.
  • M-algorithm a sub-optimal tree search algorithm
  • M-best number of "best” code-vectors are passed from one stage to the next.
  • the "best" code-vectors are selected in terms of minimum distortion. The search continues until the final stage, when only one best code-vector is determined.
  • a target vector for quantization in the current frame is the mean-removed input vector minus a predictive value.
  • the predicted value is the previous quantized vector multiplied by a known prediction matrix.
  • switched prediction there is more than one possible prediction matrix and the best prediction matrix is selected for each frame. See S. Wang, et al., "Product Code Vector Quantization of LPC Parameters," in Speech and Audio Coding for Wireless and Network Applications," Ch. 31, pp. 251-258, Kluwer Academic Publishers, 1993.
  • the present invention as claimed in the appended claims provides an improved method of vector quantization of LSF transformation of LPC coefficients by a new weighted distance measure that better correlates with subjective speech quality.
  • This weighting includes running samples from the LPC filter from an impulse and applying these samples to a perceptual weighting filter.
  • the new quantization method like the one used in the 2.4 kb/s Federal Standard MELP coder, uses multi-stage vector quantization (MSVQ) of the Line Spectral Frequency (LSF) transformation of the LPC coefficients (LeBlanc, et al., entitled “Efficient Search and Design Procedures for Robust Multi-Stage VQ or LPC Parameters for 4kb/s Speech Coding," IEEE Transactions on Speech and Audio Processing, Vol. 1, No. 4, October 1993, pp. 373-385.)
  • MSVQ multi-stage vector quantization
  • LSF Line Spectral Frequency
  • An efficient codebook search for multi-stage VQ is disclosed in US Patent Application Serial No. 09/003,172 cited above.
  • the method, described herein improves on the previous one in two ways: the use of switched prediction to take advantage of time redundancy and the use of a new weighted distance measure that better correlates with subjective speech quality.
  • the input LSF vector is quantized directly using MSVQ.
  • MSVQ the target vector for quantization in the current frame
  • the mean-removed input vector minus a predicted value, where the predicted value is the previous quantized vector multiplied by a known prediction matrix.
  • switched prediction there is more than one possible prediction matrix, and the best predictor or prediction matrix is selected for each frame.
  • both the predictor matrix and the MSVQ codebooks are switched.
  • the 10 LPC coefficients are transformed by transformer 23 to 10 LSF coefficients of the Line Spectral Frequency (LSF) vectors.
  • the LSF has 10 dimensional elements or coefficients (for 10 order all-pole filter).
  • the LSF input vector is subtracted in adder 22 by a selected mean vector and the mean-removed input vector is subtracted in adder 25 by a predicted value.
  • the resulting target vector for quantization vector e in the current frame is applied to multi-stage vector quantizer (MSVQ) 27.
  • the predicted value is the previous quantized vector multiplied by a known prediction matrix at multiplier 26.
  • the predicted value in switched prediction has more than one possible prediction matrix.
  • the best predictor (prediction matrix and mean vector) is selected for each frame.
  • both the predictor (the prediction matrix and mean vector) and the MSVQ codebook set are switched.
  • a control 29 first switches in via switch 28 prediction matrix 1 and mean vector 1 and first set of codebooks 1 in quantizer 27.
  • the index corresponding to this first prediction matrix and the MSVQ codebook indices for the first set of codebooks are then provided out of the quantizer to gate 37.
  • the predicted value is added to the quantized output ê for the target vector e at adder 31 to produce a quantized mean-removed vector.
  • the mean-removed vector is added at Adder 70 to the selected mean vector to get quantized vector X and .
  • the squared error for each dimension is determined at squarer 35.
  • the weighted squared error between the input vector X i and the delayed quantized vector X and i is stored at control 29.
  • the control 29 applies control signals to switch in via switch 28 prediction matrix 2 and mean vector 2 and codebook 2 set to likewise measure the weighted squared error for this set at squarer 35.
  • the measured error from the first pair of prediction matrix 1 (with mean vector 1) and codebooks set 1 is compared with prediction matrix 2 (with mean vector 2) and codebook set 2.
  • the set of indices for the codebooks with the minimum error is gated at gate 37 out of the encoder as encoded transmission of indices and a bit is sent out at terminal 38 from control 29 indicating from which pair of prediction matrix and codebooks set the indices was sent (codebook set 1 with mean vector 1 and predictor matrix 1 or codebook set 2 and prediction matrix 2 with mean vector 2).
  • the mean-removed quantized vector from adder 31 associated with the minimum error is gated at gate 33a to frame delay 33 so as to provide the previous mean-removed quantized vector to multiplier 26.
  • Fig. 3 illustrates a decoder 40 for use with LSF encoder 20.
  • the indices for the codebooks from the encoding are received at the quantizer 44 with two sets of codebooks corresponding to codebook set 1 and 2 in the encoder.
  • the bit from terminal 38 selects the appropriate codebook set used in the encoder.
  • the LSF quantized input is added to the predicted value at adder 41 where the predicted value is the previous mean-removed quantized value (from delay 43) multiplied at multiplier 45 by the prediction matrix at 42 that matches the best one selected at the encoder to get mean-removed quantized vector.
  • Both prediction matrix 1 and mean value 1 and prediction matrix 2 and mean value 2 are stored at storage 42 of the decoder.
  • the 1 bit from terminal 38 of the encoder selects the prediction matrix and the mean value at storage 42 that matches the encoder prediction matrix and mean value.
  • the quantized mean-removed vector is added to the selected mean value at adder 48 to get the quantized LSF vector.
  • the quantized LSF vector is transformed to LPC coefficients by transformer 46.
  • LSF vector coefficients correspond to the LPC coefficients.
  • the LSF vector coefficients have better quantization properties than LPC coefficients. There is a 1 to 1 transformation between these two vector coefficients.
  • a weighting function is applied for a particular set of LSFs for a particular set of LPC coefficients that correspond.
  • the Federal Standard MELP coder uses a weighted Euclidean distance for LSF quantization due to its computational simplicity. However, this distance in the LSF domain does not necessarily correspond well with the ideal measure of quantization accuracy: perceived quality of the processed speech signal.
  • the applicant has previously shown in the paper on the new 2.4 kb/s Federal Standard that a perceptually-weighted form of log spectral distortion has close correlation with subjective speech quality.
  • the applicant teaches herein in accordance with an embodiment a weighted LSF distance which corresponds closely to this spectral distortion. This weighting function requires looking into the details of this transformation for a particular set of LSFs for a particular input vector x which is a set of LSFs for a particular set of LPC coefficients that correspond to that set.
  • the coder computes the LPC coefficients and as discussed above, for purposes of quantization, this is converted to LSF vectors which are better behaved. As shown in Fig. 1, the actual synthesizer will take the quantized vector X and and perform an inverse transformation to get an LPC filter for use in the actual speech synthesis.
  • the optimal LSF weights for un-weighted spectral distortion are computed using the formula presented in paper of Gardner, et al., entitled, "Theoretical Analysis of the High-Rate Vector Quantization of the LPC Parameters," IEEE Transactions on Speech and Audio Processing, Vol. 3, No. 5, September 1995, pp. 367-381.
  • R A (m) is the autocorrelation of the impulse response of the LPC synthesis filter at lag m
  • R i (m) is the correlation of the elements in the ith column of the Jacobian matrix of the transformation from LSF's to LPC coefficients. Therefore for a particular input vector x we compute the weight W i .
  • perceptual weighting is applied to the synthesis filter impulse response prior to computation of the autocorrelation function R A (m), so as to reflect a perceptually-weighted form of spectral distortion.
  • the weighting W i is applied to the squared error at 35.
  • the weighted output from error detector 35 is: ⁇ W i ( X i - X i ) 2 .
  • Each entry in a 10 dimensional vector has a weight value.
  • the error sums the weight value for each element.
  • one of the elements has a weight value of three and the others are one then the element with three is given an emphasis by a factor of three times that of the other elements in determining error.
  • the weighting function requires looking into the details of the LPC to LSF conversion.
  • the weight values are determined by applying an impulse to the LPC synthesis filter 21 and providing the resultant sampled output of the LPC synthesis filter 21 to a perceptual weighting filter 47.
  • a computer 39 is programmed with a code based on a pseudo code that follows and is illustrated in the flow chart of Fig. 4.
  • An impulse is gated to the LPC filter 21 and N samples of LPC synthesis filter response (step 51) are taken and applied to a perceptual weighting filter 47 (step 52).
  • low frequencies are weighted more than high frequencies and use the well known Bark scale which matches how the human ear responds to sounds.
  • the coefficients of a filter with this response are determined in advance and stored and time domain coefficients are stored. An 8 order all-pole fit to this spectrum is determined and these 8 coefficients are used as the perceptual weighting filter.
  • the following steps follow the equation for un-weighted spectral distortion from Gardner, et al.
  • R A (m) is the autocorrelation of the impulse response of the LPC synthesis filter at lag m
  • h(n) is an impulse response
  • R 1 (m) is is the correlation function of the elements in the ith column of the Jacobian matrix J ⁇ ( ⁇ ) of the transformation from LSFs to LPC coefficients.
  • the autocorrelation function of the weighted impulse response is calculated (step 53 in Fig. 4). From that the Jacobian matrix for LSFs is computed (step 54). The correlation of rows of Jacobian matrix is then computed (step 55). The LSF weights are then calculated by multiplying correlation matrices (step 56). The computed weight value from computer 39, in Fig. 2, is applied to the error detector 35. The indices from the prediction matrix/codebook set with the least error is then gated from the quantizer 27.
  • the system may be implemented using a microprocessor encapsulating computer 39 and control 29 utilizing the following pseudo code.
  • the pseudo code for computing the weighting vector from the current LPC and LSF follows:
  • the system and method be used without switched prediction for each frame as illustrated in Fig. 5 wherein the weighted error for each frame would be determined at error detector and codebook indices with the least error would be gated out by control 29 and gate 37.
  • the LPC filtered samples of the impulse at filter 21 should be filtered by perception weighting filter 47 and processed by computer 39 using code such as described in the pseudo code to provide the weight vales.
  • the perception weighting filter may use other perceptual weighting besides the bark scale that is perceptually motivated such as weighting low frequencies more than high frequencies, or the perceptual weighting filter as is presently used in CELP coders.

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Claims (15)

  1. Procédé de quantification vectorielle de coefficients de codage par prédiction linéaire (LPC) comprenant les étapes consistant à :
    convertir des coefficients LPC en coefficients de fréquences spectrales de lignes (LSF) ;
    procurer une pluralité de matrices de prédiction et un quantificateur avec une pluralité de livres de codes pour quantifier des vecteurs cibles LSF en utilisant une quantification prédictive commutée ;
    explorer une combinaison définie d'un prédicteur livres de codes pour déterminer des vecteurs cibles LSF qui produisent en résultat une sortie quantifiée qui corresponde au mieux à des coefficients LPC en commutant à la fois les matrices de prédiction et les livres de codes, ce qui minimise une erreur quadratique ;
    appliquer lesdits vecteurs ciblés aux dits livres de codes pour obtenir des vecteurs quantifiés ;
    ladite étape d'exploration comprenant une étape consistant à déterminer l'erreur quadratique multipliée par une valeur de pondération pour chaque dimension entre les coefficients de fréquences spectrales de lignes (LSF) et la sortie quantifiée dans laquelle ladite valeur de pondération est une fonction de pondération perceptuelle ;
    et ladite étape de calcul de la détermination comprenant les étapes consistant à :
    calculer une fonction d'autocorrélation d'une réponse d'impulsion pondérée ; et calculer des poids LSF (56) à partir d'une réponse pondérée selon un mode perceptuel appliquée en entrée.
  2. Procédé selon la revendication 1 comprenant le calcul d'une matrice Jacobienne (54) pour lesdits vecteurs cibles LSF ;
       le calcul de la corrélation (55) des lignes de la matrice Jacobienne ; et
       dans lequel les poids LSF sont calculés en multipliant les matrices de corrélation.
  3. Procédé selon la revendication 1 ou la revendication 2, dans lequel ladite étape de détermination comprend les autres étapes pour trouver ladite valeur de pondération consistant à :
    appliquer une impulsion au dit filtre LPC et passer en machine N échantillons (51) de la réponse de synthèse LPC ; et
    filtrer (52) les échantillons avec un filtre perceptuel ;
    calculer la fonction d'autocorrélation (53) de la réponse d'impulsion pondérée ;
    calculer la matrice Jacobienne (54) pour lesdits vecteurs LSF ;
    calculer la corrélation (55) des lignes de la matrice Jacobienne ; et
    calculer les poids LSF (56) en multipliant les matrices de corrélation.
  4. Procédé selon la revendication 3 dans lequel l'étape (52) de filtrage des échantillons avec ledit filtre perceptuel comprend une plus grande pondération des fréquences basses que des fréquences hautes.
  5. Procédé selon la revendication 4 dans lequel l'étape (52) de filtrage des échantillons avec ledit filtre perceptuel comprend de suivre l'échelle de Bark.
  6. Procédé selon l'une quelconque des revendications précédentes dans lequel ladite étape pour procurer ledit quantificateur comprend de procurer un quantificateur vectoriel à étages multiples.
  7. Procédé selon l'une quelconque des revendications précédentes dans lequel ladite étape pour procurer ledit quantificateur comprend de procurer un quantificateur ayant un ou plusieurs ensembles de livres de codes.
  8. Quantificateur (20) pour un codeur comprenant un filtre LPC (21) et un traducteur (23) pour convertir des coefficients LPC en coefficients LSF comprenant :
    un moyen pour procurer une pluralité de matrices de prédiction et une pluralité de livres de codes (27) pour quantifier des vecteurs cibles LSF en utilisant la quantification prédictive commutée ;
    un moyen (39) pour explorer une combinaison définie d'un prédicteur/livres de codes pour déterminer des vecteurs cibles LSF qui produisent en résultat une sortie quantifiée qui corresponde au mieux à des coefficients LPC en commutant à la fois les matrices de prédiction et les livres de codes, ce qui minimise une erreur quadratique ;
    un moyen (29) pour appliquer lesdits vecteurs cibles LSF aux dits livres de codes pour procurer une sortie quantifiée ;
    ledit moyen pour explorer comprenant un moyen pour appliquer une impulsion au dit filtre LPC (21) ;
    un moyen pour passer en machine des échantillons de ladite réponse LPC ;
    un filtre perceptuel (47) pour filtrer lesdits échantillons ; et
    un moyen (35) pour calculer une fonction d'autocorrélation avec une réponse pondérée pour procurer des poids LSF obtenues par calculs à partir de la réponse pondérée selon un mode perceptuel appliquée en entrée, au filtre LPC (21).
  9. Quantificateur (20) selon la revendication 8, dans lequel le moyen (35) pour calculer une fonction d'autocorrélation peut être exploité pour calculer une matrice Jacobienne pour les vecteurs LSF, une corrélation des lignes de la matrice Jacobienne, et pour calculer les poids LSF en multipliant des matrices de corrélation.
  10. Quantificateur (20) selon la revendication 8 ou la revendication 9, pouvant en outre être exploité pour :
    passer en machine N échantillons de la réponse de synthèse LPC lors de l'application de la réponse d'impulsion pondérée ; et
    filtrer les échantillons en utilisant un filtre perceptuel (47).
  11. Quantificateur (20) selon la revendication 10, dans lequel le filtre perceptuel (47) est utilisé pour pondérer davantage des fréquences basses que des fréquences hautes.
  12. Quantificateur (20) selon la revendication 11, dans lequel le filtre perceptuel (47) suit l'échelle de Bark.
  13. Quantificateur (20) selon l'une quelconque des revendications 8 à 12, pouvant en outre être exploité pour procurer une quantification vectorielle à étages multiples.
  14. Quantificateur (20) selon l'une quelconque des revendications 8 à 13, comprenant un ou plusieurs ensembles de livres de codes (27).
  15. Codeur intégrant le quantificateur (20) selon l'une quelconque des revendications 8 à 14.
EP98306906A 1997-08-28 1998-08-27 Quantisation des coefficients de prédiction linéaire Expired - Lifetime EP0899720B1 (fr)

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TW408298B (en) * 1997-08-28 2000-10-11 Texas Instruments Inc Improved method for switched-predictive quantization
KR100464310B1 (ko) * 1999-03-13 2004-12-31 삼성전자주식회사 선 스펙트럼 쌍을 이용한 패턴 정합 방법
KR100474969B1 (ko) * 2002-06-04 2005-03-10 에스엘투 주식회사 음성신호 부호화를 위한 선 스펙트럼 계수의 벡터 양자화방법과 이를 위한 마스킹 임계치 산출 방법
CN100370517C (zh) * 2002-07-16 2008-02-20 皇家飞利浦电子股份有限公司 一种对编码信号进行解码的方法
KR100647290B1 (ko) 2004-09-22 2006-11-23 삼성전자주식회사 합성된 음성의 특성을 이용하여 양자화/역양자화를선택하는 음성 부호화/복호화 장치 및 그 방법
US8160874B2 (en) 2005-12-27 2012-04-17 Panasonic Corporation Speech frame loss compensation using non-cyclic-pulse-suppressed version of previous frame excitation as synthesis filter source
CN101320565B (zh) * 2007-06-08 2011-05-11 华为技术有限公司 感知加权滤波方法及感知加权滤波器
KR101747917B1 (ko) * 2010-10-18 2017-06-15 삼성전자주식회사 선형 예측 계수를 양자화하기 위한 저복잡도를 가지는 가중치 함수 결정 장치 및 방법
EP3621074B1 (fr) * 2014-01-15 2023-07-12 Samsung Electronics Co., Ltd. Dispositif de détermination de fonction de pondération et procédé de quantification de coefficient de codage de prédiction linéaire

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TW408298B (en) * 1997-08-28 2000-10-11 Texas Instruments Inc Improved method for switched-predictive quantization

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