EP1836699B1 - Method and device for carrying out optimized audio coding between two long-term prediction models - Google Patents

Abstract

Disclosed is a system and method for implementing compression coding of audio signals, such as speech signals, using two long-term prediction (LTP) models. The method determines the parameters of a second long-term prediction model on the basis of the parameters of at least one first LTP model. The present invention is aimed at switching from an LTP model with a single coefficient (monotap) to an LTP model with several coefficients, (multitap) and vice versa, as well as at switching between two multitap LTP models. The complexity of the method may be adjusted, especially as a function of a desired compromise between a target complexity and a desired quality. A device for implementing the method according to the invention is, moreover, very useful for multiple codings in cascade (transcodings) or in parallel (multi-codings and multi-mode codings).

Description

The present invention relates to the encoding / decoding in compression of digital audio signals, especially speech signals and / or multimedia signals, in particular for transmission or storage applications. More particularly, it relates to an efficient determination of the parameters of a second long term prediction model (or " LTP " for " Long Term Prediction "), based on the parameters of at least one first LTP prediction model.

Compression encoders use digital audio signal properties such as local stationarity, exploited by short-term prediction filters, and its harmonic structure, exploited by LTP long-term prediction filters. Typically, voiced speech sounds (such as vowels) have a long-term correlation due to vocal cord vibration. The long-term correlation is modeled by an LTP filter denoted by P (z ) which makes it possible to restore the harmonic structure by using a synthesis filter of the type: $$H_{\mathit{LT}}\left(z\right)=\frac{1}{1-P\left(z\right)}$$

The simplest form of the long-term prediction filter is the P (z) filter with a single coefficient β (also called gain) and an integer delay T such that P (z) = βz ^-T . The delay T is also called "pitch" period, or simply " pitch ".

• une modélisation à plusieurs coefficients (dite "multitap") : $P\left(z\right)={\displaystyle \sum _{i=-k}^k}{\beta }_i{z}^{-T-i},$
  
• ou encore une modélisation à retards multiples : $P\left(z\right)={\displaystyle \sum _{i=1}^k}{\beta }_i{z}^{-\mathit{iT}},$
  
• ou encore une modélisation à retard fractionnaire qui utilise des sur et sous échantillonnages avec des filtres d'interpolation : $P\left(z\right)=\beta {\displaystyle \sum _{i=0}^{2I-1}}{p}_l\left(i\right)z^{-\left(T-I+i\right)},$
  
  où pour un retard (T+l/D), de résolution 1/D, les coefficients p_l(i) sont donnés par p_l(i) = h_inter(iD-l), 0≤l≤D-1, h_inter étant un filtre d'interpolation de longueur 2ID+1.

Currently, more elaborate models aim at:

• a multi-coefficient model (called "multitap "): $P\left(z\right)={\displaystyle \underset{i=-k}{\overset{k}{\Sigma }}}{\beta }_i{z}^{-T-i},$
  
• or multiple delay modeling: $P\left(z\right)={\displaystyle \underset{i=1}{\overset{k}{\Sigma }}}{\beta }_i{z}^{-\mathit{iT}},$
  
• or fractional delay modeling that uses over and under sampling with interpolation filters: $P\left(z\right)=\beta {\displaystyle \underset{i=0}{\overset{2I-1}{\Sigma }}}{p}_l\left(i\right)z^{-\left(T-I+i\right)},$
  
  where for a delay (T + 1 / D), of resolution 1 / D, the coefficients p _l (i) are given by p _l (i) = h _inter (iD-1), 0≤l≤D-1, h _inter being an interpolation filter of length 2ID +1.

The filter parameters (delay and gain (s)) vary according to the signals to be coded and for the same signal over time. For example, in speech coding, the range of pitch periods seeks to cover the range of fundamental frequencies of the human voice (from low to high voices). For the same speaker, this frequency also varies temporally. In the same way, the coefficient (s) of the filter evolve (s) also in time.

In coding, the parameters of P (z) are determined either by an open-loop analysis or by a closed-loop analysis or most often by a combination of the two analyzes. Open loop analysis is performed by minimizing the prediction error on the signal to be modeled. Closed-loop analysis (known as "synthesis analysis" ) minimizes the squared error, usually weighted, between the voice signal to be modeled and the synthesis signal. Usually, an open loop search is first provided to determine a first estimate of the pitch called "open loop pitch". Then, a synthesis analysis search on a restricted neighborhood around this anchor value makes it possible to obtain a more precise value of the pitch. These analyzes are performed on sample blocks. The lengths of the open and closed loop analysis blocks are not necessarily equal. Often, only one open-loop analysis is performed for multiple closed-loop analyzes.

For any LTP model (monotap or multitap), the determination of the LTP parameters is very expensive in computational complexity. It usually consists of an open loop on a large block of samples followed by closed loops on several sub-sample blocks (also called sub-frames). In particular, the open loop search of the harmonic delay is a very expensive operation, coding. Usually, it requires the calculation of a function of auto-correlation of the signal for many values (in fact on range of variation of the delays). In the ITU-T G.723.1 coder, this delay range has 125 integral delays (from 18 to 142) and the open loop delay is estimated every 15 ms (ie for blocks of 120 samples). In the 8 kbit / s ITU-T G.729 coder, open-loop analysis is performed every 10 ms (at each block of 80 samples) and explores a range of 124 full delays (from 20 to 143). This operation accounts for nearly 70% of the complexity of LTP analysis for this type of coding.

Even if it is focused around the delay obtained in open loop, the closed loop is also extremely expensive in calculations and, consequently, in resources. It requires the generation of adaptive excitations and their filtering. For example, in G.723.1 encoding that uses a multitap LTP model, the closed-loop analysis jointly determines the gain vector (β _i ) and a delay λ (as a candidate pitch) of each sub-frame by exploring a dictionary of gain vectors for several candidate pitch values. This analysis accounts for nearly half of the total complexity of the 5.3 kbps G.723.1 encoder.

The complexity of the LTP analysis is particularly critical when several codings must be performed by the same processing unit such as a gateway responsible for managing many parallel communications or a server distributing a large number of multimedia contents. The problem of complexity is further increased by the multiplicity of compression formats that circulate on networks. It is then provided multiple encodings or cascading (or "transcoding") or in parallel (multiformat coding or multimode coding). Transcoding is typically used when, in a transmission chain, a compressed signal frame transmitted by an encoder can not continue in this format. Transcoding makes it possible to convert this frame into another format compatible with the rest of the transmission chain. The most basic solution (and the most common at the moment) is the end-to-end addition of a decoder and an encoder. The compressed frame, arriving in a first format, is decompressed. This decompressed signal is then re-compressed in a second format accepted later in the communication chain. This cascading of a decoder and an encoder is called "tandem". Nevertheless, this solution is very expensive in complexity (essentially because of the recoding) and degrades the quality, the second coding being indeed on a decoded signal which is a degraded version of the original signal. Moreover, a frame can meet several tandems before reaching its destination, which further increases the cost of calculation and the loss of quality. In addition, delays associated with each tandem operation accumulate and can interfere with the interactivity of communications.

As for multi-format compression systems where the same content is compressed in several formats (typically in the case of content servers that broadcast the same content in several formats adapted to the access conditions, networks and terminals of the various end users) the multi-coding operation becomes extremely complex as the number of desired formats increases, which can quickly saturate the resources of the systems. Another case of multiple-coding in parallel is the post-decision multi-mode compression according to which, at each signal segment to be coded, several modes of compression are executed and the mode which optimizes a given criterion or obtains the best compromise rate / distortion is selected. Here again, the complexity of each of the modes of compression limits the number and / or leads to elaborate a selection a priori of a very limited number of modes.

Currently, most multiple coding operations do not yet fully take into account the similarities between encoding formats, which which could however reduce the complexity and the algorithmic delay while limiting the degradation brought. For the same coding format parameter, the differences between coders reside in the modeling, the method and / or the frequency of calculation, or else the quantification.

In general, the solutions proposed today focus on limiting the number of values explored for the parameters of a second LTP model by using the parameters chosen by the first format, to reduce the complexity of the second format LTP search. .

Transcoding between two LTP monotap models is the simplest case. Most of the methods currently proposed concern the transcoding between delays, the transcoding of the LTP gain being done most often at the signal itself (we speak of a "partial" tandem). When the two models are identical (same dictionary of delays and same length of subframe), a simple copy of the bit fields of the delays of a flow of bits towards the other is enough. When the dictionaries differ in their resolution (integer or fractional 1/3, 1/6, etc.) and / or their ranges of values, a transcoding in the binary domain or parameters, with a possible transformation, is used. The transformation can be quantization, truncation, doubling, or splitting. When the subframe lengths of the two formats are different, an interpolation of the delays can be provided. For example, the delays of a first format covering an output subframe are interpolated. We can then use this interpolated delay only when it is close to the delay obtained at the previous subframe, otherwise a conventional search is conducted. Another more direct method, without interpolation, is to select a delay among these delays of the first format. This selection can be made according to several criteria: last sub-frame, sub-frame having the most samples in common with the subframe of the second format or that which maximizes a criterion dependent on the LTP gain. The determined delay is an anchor value for finding the delay of the second format. he can be used as the open loop delay of the second format around which a conventional or restricted closed-loop search is performed, or as a first estimate thereof, or as an anchoring of a delay trajectory.

In the case of a transcoding between a monotap LTP modeling and a multitap LTP modeling, it is currently expected only a simple implementation in the signal domain, because of the dissimilarity of the modelizations. Most of the existing transcoding techniques are limited to reducing the complexity of the open loop of the second format by selecting as open loop delay one of the delays of the first format or an interpolation of these delays. However, some techniques have been proposed to also reduce the complexity of the closed loop.

Dans le document référencé [1] :

• " An Efficient Transcoding Algorithm For G. 723.1 And G. 729A Speech Coders", Sung-Wan Yoon, Sung-Kyo Jung, Young-Cheol Park, and Dae-Hee Youn, Proc. Eurospeech 2001, pp.2499-2502 ,
• la recherche en boucle fermée du vecteur de gains d'un modèle multitap est restreinte à un sous-ensemble du dictionnaire des gains multitap, déterminé par le gain du modèle monotap du premier format. Cette détermination, ainsi que la composition des sous-ensembles s'effectuent comme suit : le gain global de chaque vecteur du dictionnaire de gains est calculé; puis, à partir de 170 gains globaux correspondant aux 170 vecteurs du dictionnaire, 8 sous-ensembles sont constitués et un seul de ces sous-ensembles est sélectionné en fonction du gain LTP du premier modèle monotap.

Dans une variante selon le document référencé [2] :

• " Transcoding algorithm for G723.1 and AMR speech Coders: for interoperability between VoIP and Mobile Networks", Sung-Wan Yoon and al., Proc. Eurospeech 2003, pp. 1101-1104 ,
• les sous-ensembles sont composés par apprentissage comme suit : la plage de variation du gain monotap d'un codeur NB-AMR est divisée en 8 sous-sections, puis, pour chaque sous-section, une étude statistique sur un tandem NB-AMR permet de déterminer M vecteurs de gains des dictionnaires d'un codeur selon la norme G.723.1. Ces vecteurs de gain sont statistiquement les plus probables. Le nombre M est pris égal à 40 pour le dictionnaire comportant 85 vecteurs et à 85 pour le dictionnaire comportant 170 vecteurs. Lors de la recherche du vecteur de gains optimal, l'exploration du dictionnaire est limitée au sous-ensemble associé à la sous-section à laquelle appartient le gain du codeur NB-AMR.

In the document WO-03058407 , the fractional delay λ 'of a monotap model is determined from the vector of coefficients (β _i ) of a multitap model by calculating the expression: $$\mathit{\lambda \text{'}}=\mathit{\lambda }-\frac{{\displaystyle \underset{j=-2}{\overset{2}{\Sigma }}}{\mathit{<figure-callout\; id="2"\; label="j\beta \; j"\; filenames="imgf0001.png,imgf0004.png"\; state="\{\{state\}\}">j\beta \; </figure-callout>}}_j^{}}{{\displaystyle \underset{j=-2}{\overset{2}{\Sigma }}}{\mathit{<figure-callout\; id="2"\; label="\beta \; j"\; filenames="imgf0001.png,imgf0004.png"\; state="\{\{state\}\}">\beta \; </figure-callout>}}_j^{}}$$

In the document referenced [1]:

• " An Efficient Transcoding Algorithm for G. 723.1 and G. 729A Speech Coders ", Sung-Wan Yoon, Sung-Kyo Jung, Young-Cheol Park, and Dae-Hee Youn, Eurospeech 2001, pp.2499-2502 ,
• the closed-loop search of the gains vector of a multitap model is restricted to a subset of the multitap earnings dictionary, determined by the gain of the first-form monotap model. This determination and the composition of the subsets are as follows: the overall gain of each vector of the earnings dictionary is calculated; then, from 170 global gains corresponding to the 170 vectors of the dictionary, 8 subsets are constituted and only one of these subsets is selected according to the LTP gain of the first single-ended model.

In a variant according to the document referenced [2]:

• " Transcoding algorithm for G723.1 and AMR speech coders: for interoperability between VoIP and Mobile Networks ", Sung-Wan Yoon et al., Eurospeech 2003, pp. 1101-1104 ,
• the subsets are composed by learning as follows: the range of monotap gain variation of a NB-AMR coder is divided into 8 subsections, then, for each subsection, a statistical study on a NB-AMR tandem allows to determine M vectors of gains of the dictionaries of a coder according to the norm G.723.1. These gain vectors are statistically the most likely. The number M is taken equal to 40 for the dictionary containing 85 vectors and to 85 for the dictionary containing 170 vectors. When searching for the optimal gain vector, the dictionary search is limited to the subset associated with the subsection to which the NB-AMR encoder gain belongs.

In "Low complexity intelligent transcoding between ITU-T G.729 (8kbit / s) and 3GPP NB-AMR (12.2 kbit / s) coders, CORESA 2004, an original method of conversion for fixed excitation is proposed.

To the inventors' knowledge, there is currently no transcoding technique between two multitap LTP models. As we have seen above, most of the current solutions only apply to LTP monotap models. Some techniques propose a transcoding between a multitap model and a monotap model but are limited to reducing the complexity of the search for the open-loop delay of the second format.

par un filtre monotap non fractionnaire P_mono (z) = βz^-(T-δ),
on estime un gain β et une gigue δ de retard tels que :

• P_mono(z) ≅ P_multi(z), pour tous les retards entiers T considérés.

Among the few approaches proposed to reduce the complexity of the closed loop, some are based on an approximation of a multitap LTP filter by a monotap LTP filter (fractional or not). For example, in the case of an approximation of a multitap filter: $$P_{\mathit{multi}}\left(z\right)={\displaystyle \underset{i=-k}{\overset{k}{\Sigma }}}{\beta }_i{z}^{-T-i}$$

by a monotap unfractional filter P _mono (z) = βz ^{- (T-δ)} ,
it is estimated a gain β and a delay jitter δ such that:

• P _mono (z) ≅ P _multi (z), for all integer delays T considered.

The approximation of a multitap LTP model by a monotap LTP model is already exploited since the ITU-T G.723.1 standard, in fact to estimate the adaptive pre-filter and also to control the instability of the LTP filter. Studies carried out during the design of the G.723.1 encoder have shown that it is not always possible to satisfactorily approximate a multitap LTP filter with a monotap LTP filter, over a wide range of delays, with same gain β and the same jitter δ on the delay. For the same vector of gains (β _i ), the estimate of the optimal torque (β, δ) can vary greatly according to the delay T. In the coder according to G.723.1, this difficulty could be circumvented because the control procedure stability holds the maximum gain among the estimated gains (which can then be very dissimilar) and the adaptive pre-filter is inhibited for any multiptap model gains vector when, over the range of delays considered, the estimated gains are too different or the jigs on the delay are too dissimilar or too big. If, for the modules of adaptive pre-filtering and instability control of the long-term prediction filter, it is possible to circumvent the estimation difficulty without degrading the performances, these advantages are more difficult to achieve with the module of LTP analysis itself which plays a crucial role on the quality. Thus, according to the vector of gains and / or the delay considered, the 170 global gains calculated for each vector of the 170 entries of the dictionary, as seen in the prior art above [1], can be very far from optimal gains. Similarly, according to the vector of gains (β _i ) and / or the delay λ, the calculation of the fractional delay λ ', as seen in the prior art WO-03058407 above, may lead to a poor determination of fractional delay.

Whether the approach is analytic or statistical, the approximation, over a wide range of delays, of a multitap LTP filter by a single LTP monotap filter (or the inverse approximation) is too imprecise. To solve this problem, one could, to take into account the variation of the gain β and / or the jitter δ according to the delay T, store for each delay T a pair (β, δ). However, this solution would be too expensive in storage since it would require, for each gain vector and for each delay in the range, to store a torque. In the case of the G.723.1 encoder multitap LTP filter approximation, which has two multitap dictionaries of 170 and 85 vectors, with a range of 125 delays, it would be necessary to store 31875 (= 125 * (170 + 85)) pairs. Moreover, this solution would not solve the cases where the approximation of a multitap by a monotap is really too inaccurate, even erroneous. Note that, conversely, several pairs (β, δ) can also be good approximations of a multitap LTP filter.

The present invention improves the situation.

Firstly, the present invention aims to switch from a LTP model to a single coefficient (monotap) to a multi-coefficient LTP model (multitap) and vice versa, as well as switching between two multitap LTP models. In particular, it proposes a process whose complexity can be adjusted, in particular according to a desired compromise between a targeted complexity and a desired quality. A device for implementing the method according to the invention is, moreover, very useful for multiple coding in cascade (transcoding) or in parallel (multi-coding and multi-mode coding).

Thus, the invention aims first of a coding method as defined in claim 1, according to a second format, from information obtained by the implementation of at least one coding step in a first format. The first and second formats implement, in particular for the coding of a speech signal, a step of searching LTP long-term prediction parameters by scanning at least one dictionary including candidate parameters, one of which least first and second coding format using a multi-coefficient filtering (called "multitap" above), for a fine search LTP parameters.

1. a) définir des ordres d'au moins un dictionnaire qu'utilise le second format de codage,
2. b) récupérer une information a priori, obtenue suite à la détermination des paramètres LTP au cours du codage selon le premier format, pour sélectionner au moins un ordre dudit dictionnaire,
3. c) appliquer l'ordre sélectionné aux candidats dudit dictionnaire pour choisir un nombre limité de premiers candidats, et
4. d) pour effectuer le second codage, mener la recherche LTP uniquement parmi ledit nombre limité de candidats.

According to a general definition of the invention, the method comprises the following steps:

1. a) define orders of at least one dictionary used by the second coding format,
2. b) recovering a priori information, obtained following the determination of the LTP parameters during coding according to the first format, to select at least one order of said dictionary,
3. c) applying the selected order to the candidates of said dictionary to select a limited number of first candidates, and
4. d) to perform the second coding, conduct the LTP search only among said limited number of candidates.

The invention is thus distinguished from existing solutions by the definition of orders in the dictionary and the exploitation of these orders in the dictionary search procedure.

• la figure 1a représente schématiquement un système de transcodage intelligent utilisant un dispositif de codage selon le second format au sens de l'invention,
• la figure 1b représente schématiquement un système de codage multiple en parallèle, utilisant un dispositif de codage selon le second format au sens de l'invention,
• la figure 2 illustre les étapes principales du procédé au sens de l'invention,
• la figure 3 représente schématiquement les moyens mis en oeuvre par un dispositif de codage au sens de l'invention,
• la figure 4a représentant un schéma de principe d'un codeur CELP (pour "Code Excited Linear Prediction"),
• la figure 4b représente schématiquement les étapes de l'analyse LTP d'un codeur selon la norme UIT-T G.729,
• la figure 4c représente schématiquement les étapes de l'analyse LTP d'un codeur selon la norme UIT-T G.723.1 (6,3 kbit/s),
• la figure 5a illustre une correspondance entre les trames d'un codeur selon la norme UIT-T G.723.1 (30 ms) et les trames d'un codeur selon la norme UIT-T G.729 (10 ms),
• la figure 5b illustre une correspondance entre les sous-trames du codeur G.729 (5 ms) et les sous-trames du codeur G.723.1 (7,5 ms),
• la figure 6 illustre la recherche du pitch en boucle ouverte du G.729 à partir des valeurs de pitch du G.723.1,
• les figures 7a et 7b illustrent respectivement l'association entre sous-trames paires (respectivement impaires) du codeur G.729 et le jeu de paramètres LTP issu du codeur G.723.1 en tant que codeur selon le premier format,
• la figure 8 représente un tableau d'association des sous-trames du G.723.1 (colonne de droite CD) aux sous-trames du G.729 (colonne de gauche CG),
• les figures 9a et 9b représentent des histogrammes de tailles d'exploration réduites (nombre d'occurrences en ordonnée) dans des dictionnaires (initialement de 85 vecteurs pour la figure 9a et de 170 vecteurs pour la figure 9b), et garantissant moins de 1% de réduction de qualité selon le critère CELP, et
• la figure 10 représente schématiquement la sélection de N éléments du second dictionnaire quand plusieurs ordres sont constitués, dans une réalisation particulière.

Other features and advantages of the invention will appear on examining the detailed description below, and the attached drawings in which:

• the figure 1a schematically represents an intelligent transcoding system using a coding device according to the second format in the sense of the invention,
• the figure 1b schematically represents a multiple coding system in parallel, using a coding device according to the second format in the sense of the invention,
• the figure 2 illustrates the main steps of the process within the meaning of the invention,
• the figure 3 schematically represents the means implemented by a coding device in the sense of the invention,
• the figure 4a representing a block diagram of a CELP coder (for Code Excited Linear Prediction),
• the figure 4b schematically represents the steps of the LTP analysis of an encoder according to ITU-T G.729,
• the figure 4c schematically represents the steps of the LTP analysis of an encoder according to the ITU-T G.723.1 standard (6.3 kbit / s),
• the figure 5a illustrates a mapping between ITU-T G.723.1 encoder frames (30 ms) and ITU-T G.729 encoder frames (10 ms),
• the figure 5b illustrates a correspondence between the G.729 (5ms) coder subframes and the G.723.1 (7.5ms) coder subframes,
• the figure 6 illustrates the G.729 open-loop pitch search from the G.723.1 pitch values,
• the Figures 7a and 7b respectively illustrate the association between even (odd) subframes of the G.729 encoder and the LTP parameter set from the G.723.1 encoder as an encoder according to the first format,
• the figure 8 represents an array of G.723.1 subframes (right column CD) to G.729 subframes (left column CG),
• the Figures 9a and 9b represent histograms of reduced exploration sizes (number of occurrences in ordinate) in dictionaries (initially of 85 vectors for the figure 9a and 170 vectors for the figure 9b ), and guaranteeing less than 1% reduction in quality according to the CELP criterion, and
• the figure 10 schematically represents the selection of N elements of the second dictionary when several orders are formed, in a particular embodiment.

The present invention is therefore part of the multiple coding in cascade or in parallel or in any other system using, to represent the long-term periodicity of a signal, a monotap or multitap type of modeling. The invention makes it possible from the knowledge of the parameters of a first model to determine the parameters of a second model in the case where at least one of the two models uses multitap modeling. For the sake of brevity, only the case of a transition from a first model to a second is described but it will be understood that the invention also applies in the case of passing from m (m ≥1) first models to n ( n ≥2) second models (where m and n are absolutely arbitrary).

With reference to Figures 1a and 1b we therefore consider the case of two LTP modelizations of a signal corresponding to two coding systems COD1 and COD2. It may be a transition from the first coding system COD1 to the second coding system COD2, cascaded in particular by intelligent transcoding ( figure 1a ), or in parallel in particular by optimizing the multiple coding ( figure 1b ). The first coder has performed its coding operation on a given signal (for example the original signal s _o ). There are therefore LTP parameters, denoted LTP1, chosen by the first coder COD1. This coder has determined these parameters by a technique that is specific to it during the coding process. The second coder COD2 must also perform its coding. In the case of transcoding, the second coder COD2 has only the bit stream BS1 generated by the first coder COD1 and then including the bit codes of the parameters LTP1. The invention is here applicable to intelligent transcoding. In the case of multiple coding in parallel, the second coder COD2 also has the original signal s _o (or a derived version) available to the first coder COD1 and the invention applies here to intelligent multi-coding. It is indicated that the invention can also be applied to the case particular of the multiple coding in parallel which is the multi-mode coding with a posteriori decision.

• dans le dictionnaire DIC2 du second format de codage (portant la référence 25 sur la figure 2), on détermine initialement des ordres ORD1, ORD2, ..., ORDN (étape 25b de la figure 2),
• à partir de l'un au moins des paramètres LTP1 du premier format de codage, on sélectionne au moins un ordre ORD(DIC2) du dictionnaire du second format (étape 26),
• à l'étape 27, on obtient une succession ordonnée des éléments du dictionnaire e_i ², e_j ², e_k ², ...,
• on limite avantageusement l'exploration aux premiers éléments e_i ², e_j ² du dictionnaire DIC2 ainsi ordonné (étape 29), ce nombre d'éléments étant de préférence choisi selon le compromis qualité/complexité voulu (qualité visée/complexité autorisée), à une étape 28.

The present invention relates to the determination of a parameter of a LTP model, denoted LTP2, from at least one LTP1 parameter of another LTP model, when at least one of the two models is a multitap model. Instead of looking for the parameter of the second encoding format in its definition set (or "dictionary" ), the invention provides the following steps, with reference now to the figure 2 :

• in the DIC2 dictionary of the second coding format (referenced 25 as the figure 2 ), orders ORD1, ORD2,..., ORDN are initially determined (step 25b of the figure 2 )
• from at least one of the LTP1 parameters of the first coding format, at least one ORD (DIC2) command of the second format dictionary (step 26) is selected,
• in step 27, we obtain an ordered succession of the elements of the dictionary e _i ² , e _j ² , e _k ² , ...,
• advantageously limits the exploration to the first elements e _i ² , e _j ² of the dictionary DIC2 thus ordered (step 29), this number of elements being preferably chosen according to the desired quality / complexity compromise (target quality / authorized complexity), at a step 28.

Thus, it will be understood that it is possible to limit, by the implementation of the invention, the number of elements of the second dictionary DIC2 on which the LTP search will carry during the second coding COD2, while ensuring a good quality of the coding COD2. On the figure 2 , the operations carried out respectively by the first coder COD1 and the second coder COD2, the latter having the dictionary DIC2 (reference 25), were separated into two blocks 20 and 24 respectively. For its part, the first coder COD1 determined the parameters LTP1, in step 21, using at least its dictionary DIC1 (step 22). It will then be understood that the manner in which the first coder COD1 has determined its parameters LTP1, typically from the original signal s _o , constitutes a priori information (step 23) which can be used by the second coder to order its dictionary DIC2. Finally, the parameters LTP2 obtained (step 30) by applying the dictionary classification of the second coder within the meaning of the invention, can themselves be used for the classification of a dictionary according to a third coding format (not shown), where appropriate, and so on for cascading transcoding or multiple coding in parallel.

It will be noted that the figure 2 is given here only for mainly didactic purposes. For example, the notation e _i ² , e _j ² , e _k ² , ... of the elements of the dictionary DIC2 is not really conventional, as will be seen later. Moreover, the classification of the DIC2 dictionary (step 25b) and the limitation of its elements to be taken into account for the search according to the quality / complexity criterion (step 28) can be conducted jointly substantially in one and the same step. Finally, we have shown on the figure 2 a first encoder COD1 issuing priori information (step 23) in the second coder COD2. Nevertheless, as a variant, the second coder COD2 can simply recover from the first coder COD1 the binary codes of the parameters LTP1 that the first coder has determined and retrieve this information a priori, thanks in particular to the knowledge of the type of coding and the dictionary used by the first encoder COD1.

• une mémoire MEM stockant une table de correspondance définissant, en fonction des paramètres LTP1 déterminés par le premier format de codage, des ordres d'un dictionnaire qu'utilise le second format de codage,
• des moyens, tels qu'une interface 31, pour récupérer un signal donnant au moins une information a priori sur des paramètres LTP1 au cours d'un codage selon le premier format,
• des moyens 32 actifs sur réception de ce signal pour consulter la table de correspondance et sélectionner au moins un ordre du dictionnaire du second format,
• des moyens de calcul, tel qu'un processeur 35, pour :
  • ordonner le dictionnaire 33 du second format selon l'ordre sélectionné, en vue de choisir un nombre limité de premiers candidats dans le dictionnaire 33, et
  • poursuivre le codage selon le second format, avec d'autres modules 34 le cas échéant, en menant la recherche LTP uniquement parmi ce nombre limité de candidats.

We have shown on the figure 3 a coding device according to the second format, within the meaning of the invention. This device is arranged to use coding information by implementing an encoding according to a first format (here the parameters LTP1 recovered from the coding according to the first format COD1). The device within the meaning of the invention comprises, in the example shown:

• a memory MEM storing a correspondence table defining, as a function of the parameters LTP1 determined by the first coding format, commands from a dictionary used by the second coding format,
• means, such as an interface 31, for recovering a signal giving at least a prior information on LTP1 parameters during a coding according to the first format,
• means 32 active on receiving this signal to consult the correspondence table and select at least one order of the dictionary of the second format,
• computing means, such as a processor 35, for:
  • ordering the dictionary 33 of the second format according to the selected order, in order to select a limited number of first candidates in the dictionary 33, and
  • continue coding in the second format, with other modules 34 if necessary, conducting the LTP search only among this limited number of candidates.

Of course, the processor 35 manages all or part of the modules of the device. For this purpose, it can be animated by a computer program product. The present invention also aims at such a computer program product, stored in a memory of a processing unit or on a removable medium intended to cooperate with a reader of said processing unit or downloadable from a remote site, and comprising instructions for implementing all or part of the steps of the method according to the invention.

It will be understood in particular that the device COD2, within the meaning of the invention, can directly recover the parameters LTP1 of the first coder COD1 in order to deduce the aforementioned information and, hence, the order of its dictionary DIC2, or, alternatively, receiving from the first coder COD1 directly the information a priori on the order of its dictionary, the first coder COD1. In the latter case, the first coder COD1 already plays a particular role in the invention.

The present invention also provides a system including the first encoder and the device within the meaning of the invention. Indeed, the device of the figure 3 can be inserted into a coding system implementing at least a first and a second coding format. This system then comprises at least one coding device according to the first format COD1 and a coding device in the sense of the invention and then applying second format COD2. In this respect, the invention aims at such a system. The coding device according to the first format and the coding device according to the second format can be cascaded for transcoding as shown in FIG. figure 1a . As a variant, the coding device according to the first format and the coding device according to the second format can be put in parallel, for multiple coding, as shown in FIG. figure 1 b.

In the implementation of the invention, it will be remembered that the second coder COD2 can recover from the first coder COD1 (when the latter has determined the parameters LTP1) information that will enable him to order his dictionary DIC2 (see figure 2 ). Then, a search LTP only among the first elements (e _i ² , e _j ² ) DIC2 dictionary so ordered will maintain a good quality of the second coding.

• d'ajuster librement le compromis qualité/complexité,
• ou encore, pour une complexité donnée, optimiser la qualité,
• ou, inversement, minimiser la complexité pour une qualité donnée.

Advantageously, the exploitation of the orders of the second dictionary DIC2 offers great flexibility on the number of ordered elements to be explored. It is then possible:

• to freely adjust the compromise quality / complexity,
• or, for a given complexity, to optimize the quality,
• or, conversely, minimize the complexity for a given quality.

This adjustment can be made at the beginning of treatment. It can also be performed at each block to be processed according to parameters of the first coding format and / or the characteristics of the signal to be coded (for example, according to a voicing criterion). For the same block, the complexity may also vary depending on the LTP subframes. The invention offers a great flexibility which makes it possible to dynamically distribute the computing power available between the modules of the second encoder and / or the resources for processing the LTP subframes.

Preferably, it is from an initial partition of the dictionary DIC1 associated with a parameter of the first LTP model that orders DIC2 dictionary associated with a parameter of the second model LTP are determined. It is indicated that the determination of an order consists in classifying the elements of the second dictionary DIC2 according to a certain criterion. A ranking (or "order") is given by indexing the elements of the dictionary DIC2.

Several types of partition of the first dictionary DIC1 can be provided. A first example is the elementary partition of a dictionary DIC1 of N elements in N disjoint classes of size 1. N orders of the second dictionary are then determined. More sophisticated partitions may be chosen, in particular by techniques known per se of quantization (vector or scalar) or of classification of the data.

Advantageously, it is possible to group similar orders, which amounts to modifying the initial partition of the first dictionary and, consequently, the number of orders of the second dictionary. We can also recalculate the orders once they have been grouped together. The procedures for determining the partition of the first dictionary in N classes and calculating the N commands of the second dictionary can be iterated, the number N being able to vary during the iterations. As a variant or in addition, in order to limit the memory necessary for storing the orders of the second dictionary, for each of these commands, a maximum number of elements to be retained is chosen, this number being able to differ according to the orders and / or classes of the first dictionary.

In another variant, the classes of the first dictionary are not necessarily disjoined. Typically, the same element can be associated with more than one order of the second dictionary. Choice of order or combination orders can then take into account factors other than the current LTP parameter of the first dictionary.

Initially, the number of orders and the orders that are appropriate in the second dictionary are determined by a statistical and / or analytical study, as a function of successive sets of LTP parameters according to the first model. This study defines, for each class of the partition of the dictionary associated with a LTP parameter of the first format, a classification of the dictionary of a parameter of the second format. A statistical study was carried out on an off-line bench by associating in the same coder the LTP model of the first format and the LTP model of the second format. Paralleling the two LTP analyzes was the preferred learning configuration. Of course, other configurations may be used, including a conventional tandem cascading the two encodings. The statistical study ensures, for each element of the first dictionary (or each class of its partition), a classification of the elements of the second dictionary according to a certain criterion. Preferentially, this criterion evaluates the impact on the quality of the returned signal. Indeed, the quality criterion can be that used in the coding to select the second parameter LTP. Of course, other criteria can be used, in particular the solicitation of an element of the second dictionary for a class of the first dictionary. In addition, a combination of criteria can also be used.

An analytical study can also be performed to determine orders of the second dictionary based on a partition of the first dictionary. Preferably, the analytical study complements the statistical study described above. It is preferentially limited to dictionary parts that lead to satisfactory analytical approximations.

The determination of an LTP parameter of the second coding format is now described from the LTP parameters according to the first coding format.

In the context of the design of algorithms for a restricted exploration of the second dictionary knowing the LTP parameters chosen by the first coding format, the partition of a first dictionary is preferably exploited and the commands of the second dictionary which are associated with this partition of the first dictionary.

For the sake of clarity of the presentation, the principles of the algorithm used are first described when the two coding formats have LTP subframes of identical duration. Each current subframe of the second coding format corresponds to a single sub-frame of the first coding format. For this first sub-frame, the first coding format has selected a set of parameters LTP (called "first set LTP1" ). Thanks to the partition of the dictionary associated with one of the LTP parameters of the first model, a search order of the second dictionary is selected by choosing the order associated with the class of the element of the first set LTP1. Then, the second dictionary is explored according to the order thus determined. In addition, depending on a compromise quality / complexity and / or possibly the maximum number of elements of the second dictionary retained for the class, the number of elements tested is restricted. In general, we will remember that among all the elements of the second dictionary, only the first elements determined by the order that has been chosen are tested.

When the two coding formats have LTP subframes of different durations, it happens that a current subframe of the second format may correspond to more than one subframe of the first format. This situation is illustrated on the figure 5b , for exemple. For these first subframes, the first coding format selected sets of LTP parameters. Thanks to the partition of the dictionary associated with one of the LTP parameters of the first model, it is possible to preselect exploration orders of the second dictionary by choosing the orders associated with the classes of the elements of the first games. It may be that only one order is finally selected if the parameters chosen for the first subframes belong to the same class of the partition of the first dictionary. However, this is a special case. We are then brought back to the previous diagram corresponding to LTP subframes of identical duration. If, on the contrary, more than one order has been preselected, only one order (for example the most preselected order) may be retained, or the one which corresponds to the sub-frame of the first format which covers the plus the current subframe of the second format.

Depending on the type of LTP parameter of the partition of the first dictionary, other criteria may be retained. Instead of retaining only one order, another solution is to combine at least a part of the different preselected orders. Several combination procedures are possible. For example, if K orders have been selected, we first examine the first element of each K orders, eliminating any redundancies. We obtain K ₁ elements (K _i ≤K). Then, K ₂ elements, such as K ₂ ≤ K and K ₂ ≤NK ₁ , chosen from the set consisting of the second element of the K orders (eliminating any redundancies), and so on until obtaining N elements, N being the maximum number of elements of the second dictionary to be tested. This selection of N elements e _i , e _j , ..., e _k , ... is schematically represented as the first elements of K ORD1, ORD2, ..., ORDK commands, on the figure 10 . The number N of elements retained in the set ENS can be chosen for example according to the maximum complexity allowed. In this ranking, it is also possible to focus on the items most often ranked among the first.

As a variant, it is also possible to constitute K subsets of the rankings by preselecting the N _i (≤ N) first elements of each classification C _i (1 _{i i} K K). The choice of N _i is such that ΣNi ≥ N and makes it possible to treat the rankings fairly or, on the contrary, to favor certain rankings. Then, one selects all the elements present in the K subsets, then the elements present in K-1 subsets and so on until retaining N elements. If N elements have not been so obtained, the number of elements is completed by, for example, successively taking the following elements in the K subsets.

One can obviously combine some of these ranking strategies. It is generally indicated that the second dictionary is preferably explored according to a "dynamic" order thus determined. This dynamic ordering procedure from predetermined and stored orders can also be applied when the classes of the partition are not disjoint and an element of the first dictionary belongs to more than one class.

Three cases of transition from a first LTP model to a second LTP model are described below, illustrating the application of the invention to different models and types of LTP parameters. Of course, although the examples are given only for a first and a second dictionary, the invention is easily generalized to more than a first and / or second dictionary.

* Case of the transition from a monotap model to a multitap model

The parameters of the monotap model of a COD1 format are available and it is sought to determine at a lower cost of calculation and / or resources those of the multitap model of a COD2 format. For each subframe, the coder COD1 determined the pair (λ _e , β _e ) of parameters of the LTP monotap filter. The coding of a sub-frame of COD2 requires the determination of pairs (λ _s , (β _i ) _s ) (where i is a gain index) of LTP multitap filter parameters. The set of parameters of the first model is therefore (λ _e , β _e ). The set of parameters of the second model is (λ _s , (β _i ) _s ).

The determination of the delay λ _s is done by one of the known methods of the state of the art. For example, it is possible to use the intelligent transcoding method which directly determines this delay λ _s by choosing as delay, the one determined by COD1 on its subframe which shares the most samples with the current sub-frame of COD2. (if this delay λ _e is fractional, we take its integer part or the nearest integer). This situation will be described later with particular reference to Figures 7a and 7b .

The vector of gains (β _i ) _s for each COD2 subframe from at least one of the gains β _e of the COD1 subframes is then determined with low complexity in the sense of the invention. By a study associating the two models LTP, one made a partition of the first dictionary (here the dictionary of the scalar gains β _e ). Then orders of the second dictionary associated with this partition are determined. These orders correspond here to all the gain vectors (β _i ) _s . From the scalar LTP gains β _e chosen by the first COD1 format for its subframes corresponding to a current COD2 subframe, the orders of the second dictionary associated with the classes of these scalar gains are preselected. Then, only one of these orders can be retained, or an order is dynamically constituted. Finally, the N first gain vectors determined by this order are tested to select the best vector (according to a criterion such as the usual CELP criterion). It will be recalled that, thanks to orders, the number N can be easily adjusted as a function, for example, of the desired quality / complexity compromise. In general, N is much smaller than the size of the second dictionary.

According to one of the advantages of the present invention, the optimal gain vector of a multitap LTP filter of a second coding format is thus determined from at least one gain of a monotap LTP filter of a first format, significantly reducing the exploration complexity of the second dictionary of gain vectors and limiting the number of gain vectors to be tested. Unlike the reference [2] given above, for each monotap gain, a subset of vectors of fixed size gains is associated, the solution according to the invention makes it possible to adjust the exploration of the dictionary by according to the quality sought and complexity constraints. It will be understood that the invention involves more the different orders of the dictionary of vectors of gains than predefined and fixed subsets as in the aforementioned reference.

In the case of intelligent transcoding of the 8 kbit / s ITU-T G.729 encoder to the 6.3 kbit / s ITU-T G.723.1 encoder, which will be described later as an exemplary embodiment , the steps outlined above can be applied to the focus of the closed-loop search in the two G.723.1 gain vector dictionaries from the LTP gains of the G.729 encoder.

* Case of the transition from a multitap model to a monotap model

This case is the reverse of the previous one. We have the LTP multitap model parameters of a first COD1 format and we try to determine at lower cost those of the LTP model monotap of a second format COD2. The set of parameters of the first model is therefore written (λ _e , (β _i ) _e ) (where i is a gain index), while the set of parameters of the second model is written (λ _s , β _s ) . From at least one set of parameters selected by the first coder COD1, it is sought to obtain a delay λ _s and a gain β _s for the format COD2. By a study associating the two models LTP, one made a partition of the first dictionary which is, in this case, that of the vectors of gains (β _i ) _e . For the purposes of the invention, orders of the second dictionary associated with the partition of the first dictionary are then determined. Here, the second dictionary consists of the set of jitter values (λ _e -λ _s ). From the gain vectors (β _i ) _e chosen by the first format COD1, for its subframes which correspond to the current subframe of COD2, the commands of the second dictionary associated with the classes of these gain vectors are preselected. Then, only one of these orders can be retained, or an order can be dynamically constituted. Finally, the "neighborhood" values thus determined around one or more anchoring delays λ ' _s are explored. The determination of the anchoring delay (s) is made by a method known in the state of the art.

The present invention therefore proposes an original solution making it possible to reduce the complexity of the determination of the delay λ _s by reducing the number of tested delay values of a monotap LTP model of a second coding format based on the knowledge of the parameters. of a multitap LTP model of a first coding format. Most of the methods of the prior art only use the delay without exploiting the vector of gains. As in the document WO-03058407 both types of parameters are used here. Nevertheless, contrary to the teaching of this last reference, a vector of gains points to a set of several jitter values and not to a single value as in this reference. According to one of the advantages afforded by the invention, the problems associated with the approximation of a multitap LTP filter by a single monotap filter are thus overcome.

In an advantageous variant, to limit the storage, the ordered neighborhoods are intervals of increasing size. This measurement is particularly advantageous for focusing the search in open loop and / or closed. An exemplary embodiment will be described below, relating to the closed-loop search of the LTP delay of the 8 kbit / s ITU-T G.729 encoder from the LTP parameters of the ITU-T G.723.1 6.3 kbit coder. / s.

* When moving from a multitap model to a multitap model

To the inventors' knowledge, this case has never been studied in the prior art.

We have the parameters of the multitap model of a first COD1 format and we try to determine at a lower cost those of the multitap model of a second COD2 format. The set of parameters of the first model is therefore written (λ _e , (β _i ) _e ). The set of parameters of the second model is also written (λ _s , (β _i ) _s ). From at least one set of parameters selected by the first COD1 format, it is sought to obtain a delay λ _s and a gain vector (β _i ) _s for the second format COD2.

The determination of the delay λ _s from at least one delay λ _e is done by a known method of the state of the art. It should be noted that the implementation of the present invention makes it possible here to determine with a low complexity the vector of gains (β _i ) _s for each subframe of the second format COD2 from at least one vector of gains (β _i ) _th subframes of the first COD1 format. By a study associating the two models LTP multitap, was carried out, in the sense of the invention, a partition of the first dictionary which is in this case that of the vectors of gains (β _i ) _e . The orders of the second dictionary are then determined (here that of the gain vectors (β _i ) _s ) that are associated with this partition. From the gain vectors (β _i ) _e chosen by the first format COD1 for its subframes which correspond to the current subframe of the second format COD2, the commands of the second dictionary associated with the classes of these gain vectors are preselected . Then, only one of these orders can be retained, or an order can be dynamically and evolutionarily constituted. Finally, the first earnings vectors determined by this order are tested to select the best one.

An exemplary embodiment between the bit rates 6.3 kbit / s and 5.3 kbit / s of the ITU-T G.723.1 encoder illustrating the latter case is presented below.

** EXAMPLES OF REALIZATION

Three exemplary embodiments are presented which are aimed at transcoding between two different ITU-T G.729 and ITU-T G.723.1 coding formats for the first two, and a bit rate change within a multi-bit encoder ( G.723.1) for the latter. A description of these two ITU-T coders is first given as well as their LTP modelings.

ITU-T G. 729 coders at 8 kbit / s and ITU-T G. 723.1 (6.3 kbit / s and 5.3 kbit / s)

These two coders belong to the family of CELP coders, synthesis analysis coders.

Synthetic analysis coders

In these coders, the synthesis model is used to extract the parameters modeling the signals to be coded. These signals can be sampled at the telephone frequency (F _e = 8 kHz) or a higher frequency, for example at 16 kHz for wideband coding (bandwidth 50 Hz to 7 kHz). Depending on the application and the desired quality, the compression ratio varies from 1 to 16 so that these encoders operate at rates of 2 to 16 kbit / s in the telephone band, and at rates of 6 to 32 kbit / s. enlarged band. The coding and digital decoding device of CELP type, synthesis analysis coder most currently used for the coding of the speech signals, is presented on the 4a. The speech signal s ₀ is sampled and converted into a series of blocks of (L ') samples called frames. In general, each frame is cut into smaller blocks of (L) samples, called subframes. Each block is synthesized by filtering a waveform extracted from a repertoire (also called fixed excitation dictionary), multiplied by a gain, through two filters varying in time. The excitation dictionary is a finite set of waveforms of L samples. The first filter is the long-term prediction filter. A LTP (Long Term Prediction) analysis is used to evaluate the parameters of this long-term predictor that exploits the periodicity of voiced sounds. This predictor is equivalent to a dictionary storing past excitement for different delays. This dictionary is generally called "adaptive excitation dictionary". The second filter is the short-term prediction filter. The Linear Prediction Coding ( LPC) analysis methods make it possible to obtain these short-term prediction parameters, which are representative of the vocal tract transfer function and characteristics of the signal spectrum.

So, referring to the figure 4a representing a block diagram of a CELP coder, the speech signal s ₀ undergoes the LPC analysis 41 (not shown in detail), as well as an LTP analysis with a construction of the repertoire of fixed excitations 46 and adaptive excitations 45 for supplying the synthesis filter 44. In the loop thus constituted, a perceptual weighting module 42 and an error minimization module 43 are also provided.

The method used to determine the innovation sequence is therefore the method of synthesis analysis. At the encoder, a large number of excitation dictionary innovation sequences are filtered by the two LTP and LPC filters, and the selected waveform is that producing the closest synthetic signal of the original signal according to a criterion. perceptual weighting, commonly known as the CELP criterion.

G.729 LTP model at 8 kbit / s (monotap)

] et en résolution entière dans la plage [85; 143].The ITU-T G.729 coder operates on a 3.4 kHz band-limited speech signal sampled at 8 kHz and cut into 10 ms frames (ie 80 samples per frame). Each frame is divided into two sub-frames (numbered hereinafter 0 and 1) of 40 samples (5 ms). The LTP model of the ITU-T G.729 encoder is based on fractional resolution monotap modeling. At each frame, the LTP analysis determines a delay λ _i and a gain β _i for each subframe. The figure 4b presents the main steps. At each frame, a search for the open-loop delay, denoted λ _OL , is performed in the value range [20; 143] (step 401). Then, the delay of the first sub-frame is searched in a closed loop around the open-loop delay λ _OL over the range [λ _OL -3; λ _OL +3] (step 402). Thus using the synthesis analysis, the delay λ ₀ of the even subframe is determined with a fractional resolution of 1/3 in the range [ $19\frac{1}{3};84\frac{2}{3}$

] and in full resolution in the range [85; 143].

Then, the delay λ ₁ of the second sub-frame is determined with a fractional resolution of 1/3 by synthesis analysis around λ ₀ over the range [int (λ ₀ ) -5 _2/3 ; int (λ ₀ ) +4 _2/3 ], int (λ ₀ ) being the integer part of the possibly fractional delay λ ₀ (step 404). For each subframe, the gain β is calculated once the determined closed-loop delay (steps 403 and 405). After searching for the fixed excitation, the gain β is quantized together with the gain of the fixed excitation by a seven-bit vector quantization. The definition set (or dictionary) of the G.729 monotap LTP gain is therefore 128.

LTP model of G.723.1 (multitap)

The ITU-T G.723.1 coder operates on a 3.4 kHz band-limited speech signal sampled at 8 kHz and cut into 30 ms frames (240 samples per frame). Each frame has 4 subframes of 7.5 ms (60 samples) grouped 2 by 2 in super subframes of 15 ms (120 samples). The ITU-T G.723.1 coder uses multitap 5-order modeling. The long-term predictor coefficients are vector quantized using two dictionaries previously stored at 85 or 170 inputs for the 6.3 kbit / s mode, while the 5.3 kbit / s mode uses only the 170-input dictionary. In the 6.3 kbit / s mode, the choice of the explored dictionary depends on the delay value of the even subframes.

The figure 4c illustrates the main steps of the LTP analysis of the G.723.1 encoder. At each frame, two open loop LTP analyzes (once per super subframe) are performed in order to estimate a delay λ ⁱ _OL (i = 0 or 1) over the range [18; 142] for each block of 120 samples (step 410). Then, for each super subframe, two closed loop LTPs (one for each subframe) are performed. The delays λ _2i of the even subframes (subframes 0 and 2) are searched in a closed loop around the corresponding delay λ ⁱ _OL over the range [λ ⁱ _OL -1; λ ⁱ _OL +1]. In conjunction with this research, the earnings vector dictionary is also explored by synthetic analysis (step 411). For the odd subframes (sub-frames 1 and 3), a similar search (joint search of the gain vector and the closed-loop delay) is performed and the search for a delay λ _{2i + 1} in a loop closed is limited to the vicinity of the closed-loop delay of the previous sub-frame [λ _2i -1; λ _2i + 2] (step 412).

First Embodiment: Determination of 6.3 kbit / s G.723.1 Multitap LTP Parameters from G.729 Monostap LTP Parameters at 8 kbit / s

As presented on the figure 5a , conventionally taking a common time origin, a G.723.1 coding frame corresponds to three G.729 coding frames. It thus appears that the subframes of G.729 do not coincide with those of G.723.1, but on the contrary the seconds (7.5 ms) overlap the first ones (5 ms). The figure 5b represents a frame of the G723.1 coding and three G.729 coding frames and their respective subframes. The subframes of the G.723.1 frame are numbered from 0 to 3. The three G.729 frames are grouped and their subframes are numbered from 0 to 5.

Determining the delay of the multitap filter

The determination of the delay is direct. Thus, for the even subframes of the G723.1, that is to say the subframes 0 and 2, the delay is taken equal to the integer part of that of the sub-frames 1 and 4 of G.729. For odd subframes, a closed loop is performed around the previous delay (even subframe). This closed loop can be identical to that of G.723.1, but can also be restricted according to the desired complexity, or even eliminated to keep then the same value of delay on the two even and odd subframes.

Determination of the coefficients of the multitap filter

Here, we consider only one first dictionary which is the set of 128 monotap LTP gains of the G.729 while we consider two seconds possible dictionaries (the two G.723.1 gain vector dictionaries whose choice depends on the delay of the sub-frames).

Once the delay has been determined, it is still necessary to determine a vector of 5 gains in the vector dictionary of 5 coefficients selected by the G.723.1 coder. The implementation of the present invention makes it possible to restrict its exploration to a limited number of gain vectors determined from the monotap LTP gains of the G.729 coder subframes.

Beforehand, a statistical study was carried out by combining within the same encoder the multitap model of the G.723.1 encoder and the monotap model of the G.729 encoder. This study allowed for each of the 128 G.729 monotap LTP gains to classify the 170 and 85 LTP multitap gain vectors of the two G.723.1 dictionaries according to their impact on the quality of the returned signal. Here, the CELP criterion is used for this purpose. For each of these two G.723.1 dictionaries, we obtained 128 orders (or rankings) associated with the basic partition of all 128 LTP monotap gains.

Each subframe of G.723.1 covers (at least partially) two subframes of G.729. First, we extract the two monotap gains (noted g ₁ and g ₂ ) from these two corresponding G.729 subframes. Each of these two gains is associated with a ranking C (g _i ) of the vectors of the multitap coefficient vector dictionary. This dictionary is selected by the delay value of the even subframe of G.723.1.

Let N be the maximum allowed number of multitap gain vectors for the current G.723.1 encoder subframe. If the two G.729 wins are equal, then there is only one ranking and the first N ordered by this earnings vector dictionary ranking are retained. Otherwise, we build an order of N elements from two different orders. For example, two subsets of the rankings C (g ₁ ) and C (g ₂ ) are formed by preselecting their N ₁ and N ₂ (respectively) first elements. N ₁ and N ₂ are less than or equal to N. The two can be treated equally rankings (N ₁ = N ₂ ) or favor one of the two rankings. For example, we can favor the ranking associated with the largest monotap gain (typically if g ₁ > g ₂ then 0 ≤ N ₂ <N ₁ ≤ N). We can also favor the one whose sub-frame of G.729 covers most the sub-frame of G.723.1 considered. Then, we first select all the elements belonging to the two subsets. We complete the set forming the dictionary, N, taking alternately in the two subsets the highest ranked element among the remaining. Again, one can, by completing, promote one of the two subsets. One can, of course, combine some of these strategies. For example, choose N ₁ = N ₂ but after selecting the common elements, continue with the remaining elements of one of the two rankings before possibly completing the remaining elements of the other ranking. The strategy can also vary according to the subframe of the G.723.1 considered.

Finally, the exploration of the earnings vector dictionary is limited to the N vectors determined by the "dynamic" order thus constituted. This focused exploration allows you to select the best earnings vector. Preferably, the selection criterion is the CELP criterion conventionally used by G.723.1 for exploring the dictionaries of vectors with LTP coefficients. The solution exposed here allows a very strong reduction in the complexity of LTP analysis of G.723.1 coding without compromising quality. As an example of performance, we have shown on Figures 9a and 9b , for the two dictionaries, the histogram of the exploration sizes which guarantee a loss on the CELP criterion strictly lower than 1% compared to a complete exploration. It is noted that the exploration sizes (on the abscissa) are much smaller than the total size of the dictionary. Thus, the average size is 39 for the dictionary with 85 vectors and 49 for the dictionary with 170 vectors. On the basis of learning used, the statistical study shows, even for average exploration sizes, well below the dictionary sizes (48 instead of 85 and 58 instead of 170), that the restricted exploration is optimal according to the CELP criterion (practically no loss on the CELP criterion). Focused research can therefore lead to performance equivalent to exhaustive search while exploring just over half of the size 85 dictionary and one third of the 170 dictionary. These figures illustrate the reduction in complexity that provides the implementation of the present invention.

In addition, a complete storage of the 128 orders for the two dictionaries represents a total of 128 * (170 + 85) = 32640 index values to be stored. In reality, it is not necessary to retain all these values since, as indicated above, only a limited number is necessary. Thus, for a zero loss on the CELP criterion, the tests show that it would be sufficient to store about 13582 indices. By choosing a less strong constraint on the CELP criterion, this number can be further reduced (up to 11251 values per 1% loss). It can still be greatly reduced by adopting for all monotap gains another partition than the elementary partition.

Second embodiment: determining monotap LTP parameters of G.729 at 8 kbit / s from the parameters LTP multitap G.723.1 6.3 kbit / s

In contrast to the previous embodiment, parameters of the multitap LTP model of a G.723.1 frame are available and the G.729 monotap LTP parameters are sought for three frames, that is to say six frames. sub-frames (see figure 5b ).

Determination of open loop delay

Open loop search has been eliminated. For this, each of the three G.729 frames first adopts for delay in open loop the delay of one of the subframes of the G.723.1 encoder. The correspondence between G.729 frames and G.723.1 subframe is illustrated on the figure 6 .

However, it should be noted that the delay chosen by the G.723.1 encoder may be outside the range of values allowed by the G.729 encoder. Indeed, the smallest value allowed by the G.729 encoder is 19 while it is 18 for the G.723.1 encoder. Several solutions are possible to work around this problem. Typically, one can for example double the delay from the G.723.1 coder, or more simply add 1.

Determination of closed loop delay

Once the open-loop delays have been fixed for the three G.729 encoder frames, the closed-loop search remains to be performed for each subframe. It is recalled that the value ranges are as follows: $$\begin{array}{cc}{\mathrm{<figure-callout\; id="0"\; label="\lambda "\; filenames="imgf0004.png,imgf0005.png"\; state="\{\{state\}\}">\lambda </figure-callout>}}_{\mathrm{0}}\mathrm{\in }\left[{\mathrm{\lambda }}_{\mathrm{OL}}\mathrm{-}\mathrm{3}\mathrm{;}{\mathrm{\lambda }}_{\mathrm{OL}}\mathrm{+}\mathrm{3}\right]\mathrm{and}& {\mathrm{<figure-callout\; id="1"\; label="\lambda "\; filenames="imgf0001.png,imgf0004.png"\; state="\{\{state\}\}">\lambda </figure-callout>}}_{\mathrm{1}}\mathrm{\in }\left[\mathrm{int}\left({\mathrm{\lambda }}_{\mathrm{0}}\right)\mathrm{-}\mathrm{5}\frac{\mathrm{2}}{\mathrm{3}}\mathrm{;}\mathrm{<figure-callout\; id="0"\; label="int\; \lambda "\; filenames="imgf0004.png,imgf0005.png"\; state="\{\{state\}\}">int\; </figure-callout>}\left({\mathrm{\lambda }}_{}\right)\left({\mathrm{}}_{\mathrm{0}}\right)\mathrm{+}\mathrm{4}\frac{\mathrm{2}}{\mathrm{3}}\right]\end{array}$$

The standard closed-loop search of the G.729 encoder consists firstly in successively testing all the integer values of the range (7 values for λ ₀ and 10 for λ ₁ ). Once the best integer value has been selected, the different fractions (-2/3, -1/3, 1/3, 2/3) are tested to determine the best one according to the chosen criterion, in this case the one that maximizes the criterion CELP. For the even subframe, it should be noted that the fractional part is only searched if the integer part of λ ₀ is less than 85.

Here, the first dictionary (in the definition of the invention given above) is one of the two LTP gain vector dictionaries of the G.723.1 coder, the second dictionary being one of two sets of integer values of neighborhood (or jitter) around an anchoring delay. It will be understood that the invention can easily be applied to more than one first dictionary, on the one hand, and more than one second dictionary, on the other hand.

To reduce the complexity of the closed-loop search of integer values in the vicinity of the anchor value λ '(λ _OL or int (λ ₀ )), it is proposed, within the meaning of the invention, to limit the number of values delayed integers tested by closed loops. Depending on the choice of LTP gain vector made by G.723.1, only a reduced number of values is tested. The entire delay is determined in this restricted set. Then, the fractional part is sought in a classical way.

Previously, a statistical study was carried out by associating within the same encoder the multitap model of G.723.1 and the monotap model of G.729. This study allowed, for each of the gain vectors of the two G.723.1 multitap LTP dictionaries, to establish an order of importance for the two closed-loop search neighborhoods of G.729 (even and odd subframes). neighborhood values according to their impact on the quality of the restored signal. This classification makes it possible to choose the number of values tested according to the constraints of quality and complexity and to limit, for each of the six subframes of G.729, the extent of the closed loop from the choice of the gains (β _i ) done for the subframes of G.723.1. Using the correspondence between subframes of the table of the figure 8 each G.729 sub-frame is associated with one or two G.723.1 subframes. From the vector of 5 coefficients of the gain vector (β _i ), the neighborhood values of A 'are ranked in order of decreasing importance. The number of values tested is then determined according to the targeted complexity or the quality / complexity ratio.

The association between the even (odd) subframes of the G.729 encoder and the set of parameters (λ _j , (β _i ) _j ) from the G.723.1 encoder is illustrated in FIG. figure 7a (respectively on the figure 7b ).

Note that for some subframes, the anchor value λ 'may be different from the delay λ _j of the set of parameters (λj, (β _i ) _j ) determined for the associated G.723.1 sub-frame. This point is explained later where we take into account the parity of the subframes (even or odd). In a first Alternatively, one can simply ignore a possible difference. Advantageously, in another variant, the set of ordered neighborhoods is modified according to the difference (λ _j -λ ') and the size of this set is possibly modified. Preferably, the difference (λ _j -λ ') is subtracted from each element of this ordered neighborhood according to the gains (β _i ) _j and its intersection is considered with the set of definition of neighborhoods (here the interval [-3; 3] for even subframes and the interval [-5.4] for odd subframes, as discussed below.

It is also possible to condition the use of restricted neighborhoods as a function of the difference between the two delays. The strategy can therefore be adapted to the sub-frame or the gap between the delays, or to the two criteria combined.

Even subframes

The search must be carried out around the open-loop delay λ _OL over the range [λ _OL -3; λ _OL +3]. Depending on the gain vector (s) chosen by the G.723.1 encoder, orders of all 7 jitter values (-3, -2, -1, 0, 1, 2, 3) are determined. For subframe 0 (respectively 2) of the G.729 encoder, there is only one subframe of the associated G.723.1 and therefore only one gain vector and, thus, a single order. On the other hand, two sub-frames of the G.723.1 coder are associated with the sub-frame 4 of the G.729 coder, as shown in FIG. figure 7a . Two orders of all neighborhoods are therefore pre-selected by the gain vectors (β _i ) ₂ and (β _i ) ₃ . As indicated above, we can retain a single order or combine the two orders. If we retain only the order associated with the vector (β _i ) ₃ , or if we fix λ ₂ = λ ₃ (where λ ₃ is the anchor value), no particular treatment is performed. Otherwise, the ordered set of 7 neighborhoods corresponding to (β _i ) ₂ is modified as a function of (λ ₂ -λ ₃ ). Then, one eventually completes with the set ordered according to (β _i ) ₃ . The first N elements in the order obtained are tested, the size N (N≤7) is defined according to the complexity or the compromise between quality and complexity.

Odd subframes

The search must be carried out around the integer part λ ' _2p of the previous sub-frame (pair) in the range [λ' _2p -5 _2/3 ; λ ' _2p +4 _2/3 ]. For these odd subframes, as for the even subframe 4, the delay λ _j of the set of parameters (λ _j , (β _i ) _j ) of (or of) associated G.723.1 subframes can be different from this anchor value λ ' _2p . According to the vector (s) (β _i ) _j of gains chosen by the G.723.1 coder, orders of all the jitter values are preselected and modified as a function of the difference (λ _j -λ ' _2p ). Let N (N≤10) be the maximum number of values tested.

To determine the restricted search range, the following is preferentially performed for each odd subframe.

Subframe 1:

• d'une part, le retard λ'₀ en boucle fermée du G.729 est dans le voisinage (dans l'intervalle [-3,3]) du retard en boucle ouverte (ici, pris égal à λ₀ correspondant au retard en boucle fermée du G.723.1),
• d'autre part, dans le codeur G.723.1, l'écart entre les retards en boucle fermée d'une sous-trame paire et la sous-trame impaire suivante est limité car la différence (λ₁-λ₀) est dans l'intervalle [-1, 2].

The total search range is [λ ' ₀ -5 _2/3 ; λ ' ₀ +4 _2/3 ]. Two orders corresponding to the gain vectors (β _i ) ₀ and (β _i ) ₁ are preselected. Then, the ordered neighborhoods are modified according to the differences (λ ₀ -λ ' ₀ ) and (λ ₁ -λ' ₀ ). These two differences are limited because:

• on the one hand, the G.729 closed-loop delay λ ' ₀ is in the vicinity (in the range [-3.3]) of the open-loop delay (here, taken as λ ₀ corresponding to the delay in closed loop of G.723.1),
• on the other hand, in the G.723.1 coder, the difference between the closed-loop delays of an even sub-frame and the next odd sub-field is limited because the difference (λ ₁ -λ ₀ ) is in the interval [-1, 2].

Starting from the N ₁ and N ₂ first elements of the modified neighborhoods, we create a unique ordered neighborhood of size N. We first select the values common to the two subsets, then we complete the set, if necessary, by alternatively taking the best remaining value in both subsets. The closed loop search is then conducted in the subset thus formed.

Subframe 3:

The total search range is [λ ' ₂ -5 _2/3 ; λ ' ₂ +4 _2/3 ]. An order corresponding to the gain vector (β _i ) ₂ is selected. Then, the ordered neighborhood is modified according to the difference (λ ₂ -λ ' ₂ ). Unlike the previous case, the difference between λ ₂ and λ ' ₂ can be large and the intersection of the ordered neighborhood, modified by subtracting (A ₂ -A' ₂ ), can be zero. In this case, preferentially, the search is made over the entire range [λ ' ₁ -5 _2/3 ; λ ' ₁ +4 _2/3 ]. The use of ordered neighborhoods can also be conditioned by a threshold on | λ ₂ - λ ' _{2 |} . For example, neighborhoods are restricted only if | λ ₂ - λ ' ₂ | <3; otherwise the whole range [-5.4] is explored. The choice of this variant can also depend on the authorized complexity.

Subframe 5:

The total search range is [λ ' ₄ -5 _2/3 ; λ ' ₄ +4 _2/3 ]. An order corresponding to the gain vector (β _i ) ₃ is selected. Then, the ordered neighborhood is modified according to the difference (λ ₃ -λ ' ₄ ). As in the case of subframe 1, this gap is limited. Indeed, the closed-loop delay of G.729, λ ' ₂ , is in the vicinity ([-3.3]) of the open-loop delay (here taken as the closed-loop delay λ ₃ of G.723.1). . We explore the first N values of the modified ordered set.

The solution presented here allows a very sharp reduction in the complexity of LTP analysis of G.729 coding. Compared with the exploration of complete neighborhoods, the invention makes it possible to test only 60% (respectively 40%) of the neighborhood values if the gain vector of the G.723.1 coder is in the dictionary with 170 entries (respectively 85 entries). ).

Third embodiment: determination of multitap LTP parameters of the encoder G.723.1 5.3 kbit / s from the parameters multitap LTP encoder G.723.1 6.3 kbit / s

The two models are very close and are distinguished practically only by the choice of the dictionary of multitap LTP gain vectors.

Determining the delay of the multitap filter

Similar to determining the delay of a monotap described above from the LTP multitap parameters, the delay of the even subframes can be used as the open loop delay of the super subframe and then restricted. the variation range of the closed-loop delay of the 5.3 kbit / s mode as a function of the vector of five coefficients of the filter chosen by the 6.3 kbit / s mode. Preferably, no treatment other than a simple copy of the delay is necessary. Thus, each 5.3 kbit / s subframe adopts for delay that the 6.3 kbit / s mode has chosen for the same subframe.

Determination of the coefficients of the multitap filter

Here, there is only one second dictionary which is the dictionary with 170 vectors of five coefficients of the 5.3 kbit / s mode whereas two "first dictionaries" must be considered , according to the terminology used in the general definition of the invention. These two first dictionaries are the two dictionaries of gain vectors used by the 6.3 kbit / s mode of G.723.1.

In this exemplary embodiment, it is therefore sought to determine, for the 5.3 kbit / s mode, a vector of gains in the 170-input dictionary from a gain vector selected by the 6.3 kbit / s mode in one of the two dictionaries (170 or 85 vectors).

One of the two cases may seem trivial because if the 6.3 kbit / s mode uses the same dictionary for the current subframe (the dictionary with 170 vectors), one may be tempted to choose for the 5.3 kbit mode / s the same vector as the 6.3 kbit / s mode. Nevertheless, this approach brings a significant degradation of the signal. Indeed, although the LTP modeling is identical for both modes (same dictionaries of delays and vectors of gains), it must be borne in mind that the rest of the coding process is not the same. Filtering LTP is therefore not applied to the same signal, and the choice of filter coefficient vectors for the 5.3 kbit / s mode must be expanded.

For this purpose, a study was carried out on the two dictionaries to associate with each of the vectors, a classification of the vectors of the dictionary to 170 vectors.

Thus, to select a gain vector for the 5.3 kbit / s mode, it is preferred, from the choice of the gain vector made by the 6.3 kbit / s mode, to explore in the large dictionary ( 170 vectors) that a set restricted to the first N vectors of the classification associated with the gain vector chosen by the 6.3 kbit / s mode. The size N depends on the complexity or the quality or the compromise / quality complexity desired. In this subset, the gain vector that maximizes a criterion, preferably the CELP criterion, is then selected as described above.

Claims (17)

1. Method for coding an audio signal according to a second format, on the basis of information obtained by implementing at least one step of coding according to a first format, the first and second formats implementing, in particular for the coding of a speech signal, a step of searching for LTP long-term prediction parameters by exploring at least one dictionary comprising candidate parameters, one at least of the first and second coding formats using a filtering with several coefficients for a fine search for the LTP parameters, characterized in that it comprises the following steps:

   - accessing results from a statistical and/or analytical study, conducted as a function of successive suites of LTP parameters according to the first coding format, so as to determine a number of orders and appropriate orders in a dictionary that the second coding format uses,

   - recovering an a priori information of partition of the first dictionary relating to a class of the partition to which an LTP parameter obtained in the course of the coding according to the first format belongs, obtained following the determination of the LTP parameters in the course of the coding according to the first format, so as to select at least one order of said dictionary that the second coding format uses,

   - applying the selected order to the candidates of said dictionary that the second coding format uses so as to choose a limited number of first candidates, and

   - so as to perform the second coding, conducting the LTP search only among said limited number of candidates.
2. Method according to Claim 1, characterized in that an elementary partition of the first dictionary comprising N elements, into N disjoint classes of size 1, is initially envisaged.
3. Method according to Claim 1, in which the first coding format uses a first dictionary and the second coding format uses a second dictionary, characterized in that a partition of the first dictionary into non-disjoint classes is envisaged, so that one and the same element can be associated with more than one order of the second dictionary.
4. Method according to one of Claims 1 to 3, in which the first coding format uses a first dictionary and the second coding format uses a second dictionary, characterized in that a grouping of similar orders is envisaged so as to dynamically modify the initial partition of the first dictionary and, thereby, the number of orders of the second dictionary.
5. Method according to Claim 4, characterized in that furthermore an operation is envisaged consisting in successively recalculating the orders of the second dictionary, once grouped together, and in that the initial partition of the first dictionary and/or the orders thus grouped together is dynamically modified.
6. Method according to either of Claims 4 and 5, in which the first coding format uses a first dictionary and the second coding format uses a second dictionary, characterized in that, for each of the orders of the second dictionary, a maximum number of elements of the second dictionary to be retained is chosen as a function of the classes of the first dictionary and/or of the orders of the second dictionary, so as to limit a memory resource used for storing the orders of the second dictionary.
7. Method according to one of the preceding claims, characterized in that said limited number of candidates is chosen as a function of a compromise between quality and complexity of the second coding.
8. Method according to Claim 7, in which an input signal to be coded is processed by data blocks, characterized in that said compromise is fixed dynamically, with each data block to be processed as a function of parameters of the first coding format and/or of characteristics of the signal to be coded and preferably as a function of LTP subframes that each data block comprises.
9. Method according to one of Claims 1 to 8, in which an input signal to be coded is processed by data blocks each comprising, for the first coding format, first LTP subframes and, for the second coding format, second LTP subframes, characterized in that, for first and second subframes of identical duration, to each current subframe of the second coding format there corresponds a single subframe of the first coding format, and in that:

   - the first coding format selects a first suite of LTP parameters for the current subframe,

   - on the basis of the partition by classes of the dictionary associated with one of the LTP parameters of the first format, an order of exploration of the dictionary of the second format is selected by choosing an order associated with the class of the element of said first suite, and

   - following the order thus selected, a limited number of first candidates of the dictionary of the second format is explored.
10. Method according to one of Claims 1 to 8, in which an input signal to be coded is processed by data blocks each comprising, for the first coding format, first LTP subframes and, for the second coding format, second LTP subframes, characterized in that, for first and second subframes of different durations:

    - the first coding format selects a plurality of LTP parameter suites, for first subframes corresponding substantially to a second current subframe,

    - on the basis of the partition by classes of the dictionary associated with one of the LTP parameters of the first format, orders of exploration of the dictionary of the second format are preselected by choosing the orders associated with the classes of the elements of said LTP parameter suites,

    - at least one preferred order is determined on the basis of the preselection of said orders, and

    - said dictionary of the second format is explored following the preferred order, limiting the exploration to its first elements.
11. Method according to one of the preceding claims, in which the first coding format uses a filtering with one coefficient for first LTP subframes while the second coding format uses a filtering with several coefficients for second LTP subframes, characterized in that:

    - for each first subframe, by implementing the first coding format, a pair (λ_e,β_e) of first parameters of the LTP filter with one coefficient is determined,

    - for the coding of a second current subframe, a plurality of pairs (λ_s,(β_i)_s) of parameters of the LTP filter with several coefficients is determined on the basis of the suite of parameters of the first format (λ_e,β_e), with:

    - a determination of an LTP delay λ_s corresponding preferably to that determined by the first coding format on a first subframe which most overlaps the second current subframe,

    - a determination of a vector of gains (β _i )_s for the second current subframe on the basis of one at least of the gains β_e of the first subframes, by implementing steps b), c) and d) where the orders of the dictionary of the second format correspond to a set of gain vectors (β _i )_s of the second subframe.
12. Method according to Claim 11, characterized in that, for the coding of a second current subframe:

    - on the basis of first LTP gains of the first format (β_e) that are chosen for one or more first subframes corresponding to a second current subframe, the orders of the dictionary of the second format, that are associated with classes of the first LTP gains, are preselected,

    - a single one of these orders is constructed, preferably dynamically, on the basis of said orders preselected for said second current subframe, and

    - N first vectors of seconds gains, determined by the order constructed, are tested so as to select, according to a chosen criterion, a better vector of gains to be associated with the second subframe.
13. Method according to one of Claims 1 to 10, in which the second coding format uses a filtering with one coefficient for second LTP subframes while the first coding format uses a filtering with several coefficients for first LTP subframes, characterized in that:

    - for each first subframe, by implementing the first coding format, a first suite of LTP parameters λ_e,(β _i )_e corresponding to a pair comprising an LTP delay λ_e and a vector of associated gains (β _i )_e of the LTP filter with several coefficients is determined,

    - a partition of a dictionary of the gain vectors (β _i )_e of the first format is performed,

    - for the coding of a second current subframe by the second format, orders of a dictionary of the second format are determined for first subframes corresponding to the second current subframe, said dictionary of the second format being constructed from a set of jitter values and said orders of this dictionary being associated with the partition of the dictionary of the first format,

    - an order of the jitter values is determined and LTP delay values for the second format are explored successively on the jitter values thus ordered and about one or more anchoring delays determined as a function of the delays λ_e on the first subframes.
14. Method according to one of Claims 1 to 10, in which the first coding format uses a filtering with several coefficients on first LTP subframes and the second coding format uses a filtering with several coefficients on second LTP subframes, characterized in that:

    - on the basis of at least one first suite of parameters selected by the first format and including at least one vector of gains (β _i )_e determined for at least one first subframe, a partition is conducted of the dictionary of the first format corresponding to a dictionary of the gain vectors of the first format (β _i )_e,

    - orders of the dictionary of the second format corresponding to a dictionary of the gain vectors (β _i )_s of the second format are deduced therefrom, said orders being associated with said partition,

    - on the basis of gain vectors (β _i )_e chosen by the first format for first subframes which substantially cover the second current subframe, orders of the second dictionary that are associated with classes of said partition are preselected,

    - one of the preselected orders is retained,

    - several gain vectors to be associated with the second current subframe are determined as a function of the order retained, and

    - by tests on said several gain vectors, the best gain vector is selected according to a chosen criterion.
15. Device for coding an audio signal according to a second format, designed to use coding information obtained by implementing a coding according to a first format, the first and second formats implementing, in particular for the coding of a speech signal, a search for LTP long-term prediction parameters by exploring a dictionary comprising candidate parameters, one at least of the first and second coding formats using a filtering with several coefficients for a fine search for the LTP parameters, characterized in that it comprises:

    - a memory storing a correspondence table defining, as a function of LTP parameters determined by the first coding format, orders of a dictionary that the second coding format uses, the correspondence table being defined on the basis of results of a statistical and/or analytical study, conducted as a function of successive sets of LTP parameters according to the first coding format, so as to determine a number of orders and appropriate orders in a dictionary used by the second coding format,

    - means for recovering a signal giving at least one a priori partitioning information of the first dictionary relating to a class of the partition to which an LTP parameter obtained in the course of the coding according to the first format belongs, and obtained following the determination of the LTP parameters in the course of a coding according to the first format, so as to select at least one order of said dictionary that the second coding format used,

    - means active on reception of said signal for consulting said correspondence table and selecting at least one order of said dictionary that the second coding format uses,

    - calculation means for:

    - ordering said dictionary that the second coding format uses according to the selected order, with a view to choosing a limited number of first candidates from the dictionary, and

    - continuing the coding according to the second format, by conducting the LTP search only among this limited number of candidates.
16. Coding system implementing at least one first and one second coding format, characterized in that it comprises at least one device for coding according to the first format and a coding device according to Claim 15, applying said second format.
17. Computer program product, stored in a memory of a processing unit or on a removable medium intended to cooperate with a reader of said processing unit or downloadable from a remote site, characterized in that it comprises instructions for implementing all or some of the steps of the method according to one of Claims 1 to 14.

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