FR2796191A1 - AUDIO CODING AND DECODING METHODS AND DEVICES - Google Patents

Abstract

The invention concerns a method wherein a decoder synthesises a set of successive frames of N samples of an audio signal from encoding data included in a digital flow received from the encoder and comprising, for only one subset of frames, data representing spectral amplitudes of the audio signal. The decoder determines for each of the frames of said subset, on the basis of the encoding data, the cepstral coefficients (cx_q[n]) representing some at least of said spectral amplitudes whereas for the frames not forming part of said subset, it interpolates said cepstral coefficients, and generates by means of said interpolated cepstral coefficients (cx[n-1/2] a spectral estimate of the audio signal which it transforms in the temporal domain to obtain the synthesised frame.

Description

AUDIO CODING AND DECODING METHODS AND DEVICES

  The present invention relates to the field of coding audio signals. It applies in particular, but not exclusively, to speech coding, in narrowband or wideband, in various ranges of coding rate. The design of an audio codec is primarily aimed at providing a good compromise between the flow rate of the stream transmitted by the encoder and the quality of the signal.

  audio that the decoder is able to reconstruct from that stream.

  In this respect, encoder families have been developed based on an analysis of the audio signal in the spectral domain: the encoder estimates a fundamental frequency of the signal, representing its pitch, and the spectral analysis consists of determining parameters representing the harmonic structure of the signal at frequencies which are integer multiples of this fundamental frequency. A modeling of the non-harmonic component, or voiceless, can also be performed in the spectral domain. The parameters transmitted to the decoder typically represent the spectrum module of the voiced and unvoiced components. There is added information representing either voiced / unvoiced decisions relating to different portions of the spectrum, or information on the probability of voicing of the signal, allowing the decoder to determine in which portions of the spectrum it must use the voiced component. or the

unvoiced component.

  These families of encoders include encoders of the type MBE ("Multi-Band Excitation"), or the type of encoders STC ("Sinusoidal Transform Coder"). As a reference, mention may be made of US patents

  4,856,068, 4,885,790, 4,937,873, 5,054,072, 5,081,681, 5,195,166, 5,216,747,

  226,084, 5,226,108, 5,247,579, 5,473,727, 517,511, 5,630,011, 5,630,012,

  649,050, 5,651,093, 5,664,051, 5,664,052, 5,684,926, 5,701,390, 5,715,365,

  749,065, 5,752,222, 5,765,127, 5,774,837 and 5,890,108. An object of the present invention is to allow, in an analysis scheme for analysis in the spectral domain, to improve the modeling of the

  phases of the signal spectrum by the decoder.

  The invention thus proposes a method for decoding an input digital stream representing an encoded audio signal, in which a set of successive frames of N audio signal samples is synthesized from coding data included in the digital input stream, wherein the encoding data comprises, for only a subset of the frames, data representative of spectral amplitudes associated with frequencies of the spectrum of the audio signal. According to the invention, for each of the frames said subset, on the basis of the encoding data, cepstral coefficients representative of at least some of said

  spectral amplitudes. For frames not forming part of that sub-

  together, these cepstral coefficients are interpolated, and a spectral estimate of the audio signal is generated using the interpolated cepstral coefficients.

  transforms in the time domain to obtain the synthesized frame.

  The method is advantageously applied when the coding data directly comprise quantization data of the cepstral coefficients. But if spectrum modeling uses other quantized parameters in the stream that allow, by a known transformation. to recover the cepstral coefficients, for example the LSP ("Line Spectrum Parameters"), the decoder can use this transformation to proceed with the interpolation of parameters in the cepstral domain and to benefit from

thus advantages of the invention.

  In an advantageous embodiment of the method, interpolated cepstral coefficients are corrected for frames not forming part of said subset based on interpolation error quantization data included in the encoding data. Alternatively, the cepstral coefficients can be interpolated by a filter determined on the basis of

  Quantization data included in the coding data.

  Another aspect of the present invention relates to a method of encoding an audio signal, wherein a spectrum of the audio signal is determined by a frequency domain transform of a frame of the audio signal, and included in a stream. digital output of data representative of spectral amplitudes associated with at least some of the frequencies of the spectrum, wherein the spectrum of the audio signal is determined for a set of successive frames of N samples of the audio signal, and in which for each of the frames of said set of cepstral coefficients representative of at least some of said spectral amplitudes. The data representative of the spectral amplitudes are included in the output digital stream for only a subset of the frames. For the frames not belonging to said subset, the output digital flow is included either with quantization data of an interpolation error of said cepstral coefficients, or data representing a

  optimal interpolator filter determined for said cepstral coefficients.

  The invention also proposes an encoder and an audio decoder comprising means for implementing the above methods. Other features and advantages of the present invention

  will appear in the following description of examples of non

  with reference to the accompanying drawings, in which: - Figure 1 is a block diagram of an audio encoder according to the invention - Figures 2 and 3 are diagrams illustrating the formation of audio signal frames in the encoder of the figure 1; FIGS. 4 and 5 are graphs showing an exemplary spectrum of the audio signal and illustrating the extraction of the upper and lower envelopes of this spectrum; FIG. 6 is a block diagram of an example of quantization means that can be used in the coder of FIG. 1; FIG. 7 is a block diagram of means that can be used to extract

  parameters relating to the phase of the non-

  harmonic in a variant of the encoder of Figure 1; FIG. 8 is a block diagram of an audio decoder corresponding to the coder of FIG. 1; FIG. 9 is a flowchart of an exemplary procedure for smoothing spectral coefficients and extracting minimum phases implemented in the decoder of FIG. 8; FIG. 10 is a block diagram of spectral analysis and mixing modules of harmonic and non-harmonic components of the audio signal; FIGS. 11 to 13 are graphs showing examples of nonlinear functions that can be used in the analysis module of FIG.

10;

  FIGS. 14 and 15 are diagrams illustrating one way of temporally synthesizing the signal frames in the decoder of FIG. 8; FIGS. 16 and 17 are graphs showing windowing functions that can be used in the synthesis of the frames according to FIGS. 14 and 15; FIGS. 18 and 19 are schematic diagrams of interpolation means that can be used in an alternative embodiment of the encoder and the decoder; FIG. 20 is a block diagram of interpolation means that can be used in another embodiment of the encoder; and FIGS. 21 and 22 are diagrams illustrating another way of temporally synthesizing the signal frames in the decoder

  of Figure 8, using interpolation of parameters.

  The encoder and decoder described below are digital circuits which, as is usual in the field of audio signal processing, can be implemented by programming a digital signal processor.

  (DSP) or an application specific integrated circuit (ASIC).

  The audio coder shown in FIG. 1 processes an input audio signal x which, in the nonlimiting example considered hereinafter, is a speech signal. The signal x is available in digital form, for example at a sampling frequency Fe of 8 kHz. It is for example delivered by an analog-digital converter processing the amplified output signal of a microphone. The input signal x can also be formed from another

  version, analog or digital, coded or not, of the speech signal.

  The encoder comprises a module 1 which forms successive audio signal frames for the various processes performed, and an output multiplexer 6 which delivers an output stream D containing for each frame sets of quantization parameters from which a decoder will be able to of

  synthesize a decoded version of the audio signal.

  The structure of the frames is illustrated in FIGS. 2 and 3. Each frame 2 is composed of a number N of consecutive samples of the audio signal x. The successive frames have mutual time offsets corresponding to M samples, so that their overlap is of L = N-M samples of the signal. In the example considered, where N = 256, M = 160 and L = 96, the duration of the frames 2 is N / Fe = 32 ms, and one frame is

formed all M / Fe = 20 ms.

  Conventionally, the module 1 multiplies the samples of each frame 2 by a windowing function fA, preferably chosen for its good spectral properties. The samples x (i) of the frame being numbered from i = 0 to i = N-1, the analysis window fA (i) can thus be a Hamming window, of expression: fA (i) = 0.54 + 0.46. cos2i - (N) 2 (1) or a Hanning window of expression: = lr CS2i ((NN), (2) or a Kaiser window, of expression I ioH ii- (N-1) / 2J20 fAffl) = o (c) (3) (where o is a coefficient for example equal to 6, and lo (.) denotes the function of

Bessel of index 0.

  The encoder of FIG. 1 carries out an analysis of the audio signal in the spectral domain. It comprises a module 3 which calculates the Fast Fourier Transform (TFR) of each signal frame. The signal frame is formatted before being submitted to the TFR module 3: the module 1 adjoins N = 256 samples to zero in order to obtain the maximum resolution of the Fourier transform, and it also performs a circular permutation of the 2N = 512 samples to compensate for the phase effects resulting from the analysis window. This modification of the frame is illustrated in FIG. 3. The frame whose fast Fourier transform is calculated on 2N = 512 points starts with the N / 2 = last 128 weighted samples of the frame, followed by the N = 256 samples at zero, and ends with the first N12 = 128

  weighted samples of the frame.

  The TFR 3 module obtains the spectrum of the signal for each frame whose modulus and phase are respectively denoted IX1 and cpx, or IX (i) 1 and (px (i) for the frequency indices i = 0 to i = 2N-1 (thanks to the symmetry of the Fourier transform and the frames, we can limit ourselves to the values for

0 <i <N).

  A fundamental frequency detector 4 estimates for each signal frame a value of the fundamental frequency Fo. The detector 4 can apply any known method of analyzing the speech signal of the frame to estimate the fundamental frequency Fo, for example a method based on the autocorrelation function or the AMDF function, possibly preceded by a module bleaching by linear prediction. The estimation can also be carried out in the spectral domain or in the cepstral domain. Another possibility is to evaluate the time intervals between the consecutive breaks of the speech signal attributable to closures of the speaker's glottis occurring during the duration of the frame. Well known methods that can be used to detect such micro-breaks are described in the following articles: M. Basseville et al., "Sequential detection of abrupt changes in spectral characteristics of digital signals" (IEEE Trans. IT-29, No. 5, pages 708-723); R. Andre-Obrecht, "A new statistical approach for automatic segmentation of continuous speech" (IEEE Trans., Acous., Speech and Sig Proc., Vol 36, No. 1, January 1988); and C. MURGIA et al., "An algorithm for the estimation of glottal closure instants using the sequential detection of abrupt changes in speech signais" (Signal

  Processing VII, 1994, pages 1685-1688).

  The estimated fundamental frequency F0 is quantized, for example scalar, by a module 5, which supplies the output multiplexer 6 with an iF index for quantizing the fundamental frequency for each frame

of the signal.

  The encoder uses cepstral parametric modelings to represent an upper envelope and a lower envelope of the audio signal spectrum. The first step of the cepstral transformation consists in applying to the signal spectrum module a spectral compression function, which can be a logarithmic or root function. The module 8 of the encoder thus operates, for each value X (i) of the signal spectrum (0 <i <N), the following transformation: LX (i) = bLogX (i) l) (4) in the case of a logarithmic compression or LX (i) = IX (i) / (5) in the case of a root compression, y being an exponent between 0 and 1. The compressed spectrum LX of the audio signal is processed by a module 9 which extracts spectral amplitudes associated with the harmonics of the signal corresponding to the multiples of the estimated fundamental frequency F0. These amplitudes are then interpolated by a module 10 in order to obtain a

  compressed upper envelope denoted LXsup.

  It should be noted that the spectral compression could similarly be performed after the determination of the amplitudes associated with the harmonics. It could also be done after the interpolation,

  which would only change the shape of the interpolation functions.

  The module 9 for extracting the maxima takes into account the possible variation of the fundamental frequency on the analysis frame, errors that can be made by the detector 4, as well as inaccuracies related to the discrete nature of the frequency sampling. For this, the search for the amplitudes of the spectral peaks does not consist simply in taking the values LX (i) corresponding to the indices i such that i.Fe / 2N is the frequency closest to a harmonic of frequency k.F0 (k > 1). The spectral amplitude chosen for a harmonic of order k is a local maximum of the modulus of the spectrum in the vicinity of the frequency k.Fo (this amplitude is obtained directly in compressed form when the spectral compression 8 is

  performed before extraction of maxima 9).

  FIGS. 4 and 5 show an example of the shape of the compressed spectrum LX, where it can be seen that the maximum amplitudes of the harmonic peaks do not necessarily coincide with the amplitudes corresponding to the integer multiples of the estimated fundamental frequency Fo. The flanks of the peaks being quite steep, a small positioning error of the fundamental frequency Fo, amplified by the harmonic index k, can strongly distort the estimated upper envelope of the spectrum and cause a poor modeling of the signal's signal structure. . For example, taking the spectral amplitude directly for the frequency 3.F0 in the case of FIGS. 4 and 5 would produce a significant error in the extraction of the upper envelope in the vicinity of the harmonic of order k = 3, then that it is an energetically important zone in the drawn example. By interpolating from the true maximum, we avoid this kind

  estimation error of the upper envelope.

  In the example shown in FIG. 4, the interpolation is performed between points whose abscissa is the frequency corresponding to the maximum of the amplitude of a spectral peak, and whose ordinate is this maximum, before or 8 -

after compression.

  The interpolation performed to calculate the upper envelope LXsup is a simple linear interpolation. Of course another form

  interpolation could be used (eg polynomial or spline).

  In the preferred variant shown in FIG. 5, the interpolation is performed between points whose abscissa is a frequency k.Fo which is a multiple of the fundamental frequency (in fact the closest frequency in the discrete spectrum) and whose ordinate is the maximum amplitude, before or after

  compression, the spectrum in the vicinity of this multiple frequency.

  Comparing FIGS. 4 and 5, it can be seen that the extraction mode according to FIG. 5, which repositioned the peaks on the harmonic frequencies, led to a better accuracy on the amplitude of the peaks that the decoder will attribute to the multiple frequencies of the fundamental frequency. There may be a slight shift in frequency of the position of these peaks, which is not perceptually very important and is not avoided either in the case of Figure 4. In the case of the figure 4, the anchor points for the interpolation are confused with the vertices of the harmonic peaks. In the case of FIG. 5, it is required that these anchor points be precisely at the frequencies that are multiples of the fundamental frequency, their amplitudes

corresponding to those of the peaks.

  The search interval of the amplitude maximum associated with a harmonic of rank k is centered on the index i of the frequency of the TFR closest to k.F0, that is to say i = 2NkF + I where LaJ denotes the equal integer or L Fe 2j immediately below the number a. The width of this search interval depends on the sampling frequency Fe, the size 2N of the TFR and the range of possible variation of the fundamental frequency. This width is typically of the order of ten frequencies with the examples of values previously considered. It can be made adjustable according to the

  value F0 of the fundamental frequency and the number k of the harmonic.

  In order to improve the resolution in the low frequencies and thus to represent more accurately the amplitudes of the harmonics in this zone, a non-linear distortion of the frequency scale is operated on the upper envelope compressed by a module 12 before the The module 13 performs the inverse fast Fourier transform (TFRI) providing the 9-

Cepstral coefficients cxsup.

  Nonlinear distortion can more effectively minimize the modeling error. It is for example carried out according to a frequency scale of Mel or Bark type. This distortion may possibly depend on the estimated fundamental frequency F0. Figure 1 illustrates the case of the Mel scale. The relation between the frequencies F of the linear spectrum, expressed in hertz, and the frequencies F of the scale Mel is the following: Fo = (2) 'x log10 o1 + F (6) In order to limit the transmission rate, a truncation of the cepstral coefficients cx_sup is performed. The TFRI 13 module needs to calculate only a cepstral vector of NCS cepstral coefficients of orders 0 to

  NCS-1. For example, NCS can be equal to 16.

  A post-filtering in the cepstral domain, called post-liftrage, is applied by a module 15 to the compressed upper envelope LXsup. This post-scaling corresponds to a manipulation of the cepstral coefficients cx_sup delivered by the TRFI module 13, which corresponds approximately to a post-filtering of the harmonic part of the signal by a transfer function having the classical form: / A (z / y) 21 (7 H (z) = (1 - lz-1 A (z /, y) (7) where A (z) is the transfer function of a linear prediction filter of the audio signal, y1 and y2 are coefficients between 0 and 1, and p is a possibly zero pre-emphasis coefficient The relation between the post-lifted coefficient of order i, noted cp (i), and the corresponding cepstral coefficient c (i) = cx_sup (i) delivered by the module 13 is then: cp (O) = c (O)

(8)

  cp (i) 11 -Y1) c (i) p for i> 0 (8) The optional pre-emphasis coefficient [t can be controlled by the constraint of preserving the value of the cepstral coefficient cxsup (1) relative to the slope. Indeed, the value c (1) = cxsup (1) of a white noise filtered by the pre-emphasis filter corresponds to the pre-emphasis coefficient. We can choose the latter as follows:

= (, y2-1) c (1).

- 10 -

  After the post-lifter 15, a normalization module 16 further modifies the cepstral coefficients by imposing the exact modeling constraint of a point of the initial spectrum, which is preferably the most energetic point among the spectral maxima extracted by the module 9 In practice, this

  This normalization only changes the value of the coefficient cp (0).

  The normalization module 16 operates in the following way: it recalculates a value of the spectrum synthesized at the frequency of the maximum indicated by the module 9, by Fourier transform of the truncated and post-lifted cepstral coefficients, taking into account the nonlinear distortion of the frequency axis; it determines a gain of normalization gN by the logarithmic difference between the value of the maximum provided by the module 9 and this

  recalculated value; and he adds the gain gN to the post-filtered cephalal coefficient cp (0).

  This standardization can be seen as part of post-layering.

  The post-lifted and normalized cepstral coefficients are quantified by a module 18 which transmits quantization indices

  corresponding icxs to the output multiplexer 6 of the encoder.

  The module 18 can operate by vector quantization from cepstral vectors formed of post-lifted and normalized coefficients, denoted here cx [n] for the n-rank signal frame. By way of example, the cepstral vector cx [n] of NCS = 16 cepstral coefficients cx [n, 0], cx [n, 1], ..., cx [n, NCS-1] is distributed in four sub-groups. cepstral vectors each containing four consecutive order coefficients. The cepstral vector cx [n] can be processed by the means represented in FIG. 6, forming part of the quantization module 18. These means implement, for each component cx [n, i], a predictor of the form: cx p [n, i] = (1 - c (i)). rcx [n, i] + c (i). rcx [n-1, i] (9) where rcx [n] designates a residual prediction vector for the n-rank frame

  whose components are respectively denoted rcx [n, 0], rcx [n, 1], ...

  rcx [n, NCS-1], and cu (i) denotes a prediction coefficient chosen to be representative of a supposed inter-frame correlation. After quantization of the residues, this residual vector is defined by: rcx [n, i] = cx [n, i] - c (i). rcx-q [n-1, i] (10) 2 - ea (i) where rcxq [n-1] denotes the quantized residual vector for the frame of rank n-i,

- 11 -

  whose components are respectively denoted rcxq [n, 0], rcxq [n, 1], ...

rcxq [n, NCS-1].

  The numerator of the relation (10) is obtained by a subtractor 20, whose components of the output vector are divided by the quantities 2c (i) into 21. For the purposes of the quantification, the residual vector rcx [n] is subdivided into four subvectors, corresponding to the subdivision into four cepstral subvectors. On the basis of a dictionary obtained by prior learning, the unit 22 proceeds to the vector quantization of each sub-vector of the residual vector rcx [n]. This quantization can consist, for each sub-vector srcx [n], of selecting in the dictionary the quantized sub-vector srcxq [n] which minimizes the quadratic error lsrcx [n] - srcx_q [n] l2. The set icxs of the quantification index icx, corresponding to the addresses in the dictionaries of the sub-vectors

  quantized residuals srcxq [n], is supplied to the output multiplexer 6.

  The unit 22 also delivers the values of the quantized residual sub-vectors, which form the vector rcxq [n]. This latter is delayed by one frame at 23, and its components are multiplied by the coefficients oa (i) at 24 to supply the vector at the negative input of the subtractor 20. This latter vector is, on the other hand, supplied to a adder 25, whose other input receives a vector formed by the components of the quantized residual rcxq [n] respectively multiplied by the quantities 1-cc (i) in 26. The adder 25 thus delivers the

  cepstral vector quantized cxq [n] that will recover the decoder.

  The coefficient of prediction co (i) can be optimized separately for each of the cepstral coefficients. Quantization dictionaries can

  also be optimized separately for each four cepstral subvectors.

  On the other hand, it is possible, in a manner known per se, to normalize the cepstral vectors before applying the prediction / quantization scheme, starting from

the variance of the cepstres.

  It should be noted that the above scheme of quantization of cepstral coefficients may be applied only for some frames only. For example, a second quantization mode can be provided, as well as a selection process for the one of the two modes, which minimizes a least squares criterion with the cepstral coefficients to be quantized, and transmits with the quantization indices of the frame a bit indicating which one

modes has been selected.

  The quantified cepstral coefficients cxsup_q = cxq [n] provided by the adder 25 are addressed to a module 28 which recalculates the spectral amplitudes associated with one or more of the harmonics of the fundamental frequency F0 (FIG. 1). These spectral amplitudes are for example calculated in compressed form, by applying the Fourier transform to the quasispersed coefficients, taking into account the non-linear distortion of the frequency scale used in the cepstral transformation. The amplitudes thus recalculated are supplied to an adaptation module 29 which compares them with amplitudes of maxima determined by the extraction module 9. The adaptation module 29 controls the post-lifter 15 so as to minimize a module gap. between the spectrum of the audio signal and the corresponding module values calculated at 28. This module difference can be expressed as a sum of absolute values of amplitude differences, compressed or not, corresponding to one or more of the harmonic frequencies. This sum can be weighted according to the amplitudes

  spectrum associated with these frequencies.

  Optimally, the module gap taken into account in the adaptation

  Post-Lifting would take into account all the harmonics of the spectrum.

  However, in order to reduce the complexity of the optimization, the module 28 can resynthesize the spectral amplitudes only for one or more multiple frequencies of the fundamental frequency Fo, selected on the basis of the importance of the modulus of the spectrum in absolute value. The adaptation module 29 can for example consider the three most spectral peaks

  intense in calculating the module deviation to be minimized.

  In another embodiment, the adaptation module 29 estimates a spectral masking curve of the audio signal by means of a psychoacoustic model, and the frequencies taken into account in the calculation of the module deviation to be minimized are selected on the basis of the importance of the spectrum module relative to the masking curve (it is possible, for example, to take the three frequencies for which the spectrum modulus exceeds the maximum of the masking curve). Different classical methods

  can be used to calculate the masking curve from the audio signal.

  One can for example use the one developed by J.D. Johnston ("Transform Coding of Audio Signals Using Perceptual Noise Criteria", IEEE Journal on

- 13 -

  Selected Area in Communications, Vol. 6, No. 2, February 1988).

  To carry out post-scaling adaptation, the module 29 can use a filter identification model. A simpler method is to predefine a set of post-scaling parameter sets, i.e., a set of pairs y1, Y2 in the case of post-scaling according to the relationships (8), to perform the operations the modules 15, 16, 18 and 28 for each of these sets of parameters, and retain that of the sets of parameters which leads to the minimum module gap between the signal spectrum and the recalculated values. The quantization indices provided by the module 18 are then

  those that relate to the best set of parameters.

  By a process analogous to that of extracting the coefficients cx_sup representing the compressed upper envelope LXsup of the signal spectrum, the coder determines coefficients cx_inf representing a compressed lower envelope LX_inf. A module 30 extracts from the compressed spectrum LX spectral amplitudes associated with frequencies located in areas of the intermediate spectrum with respect to the frequency multiples of the frequency

fundamental estimated F0.

  In the example illustrated in FIGS. 4 and 5, each amplitude associated with a frequency located in an intermediate zone between two successive harmonics k.F0 and (k + 1) .F0 simply corresponds to the frequency modulus for the frequency (k + 1/2) .F0 located in the middle of the interval separating the two harmonics. In another embodiment, this amplitude could be an average of the spectrum modulus on a small beach surrounding this

frequency (k + 1/2) .Fo.

  A module 31 interpolates, for example linearly, the spectral amplitudes associated with the frequencies located in the zones

  intermediates to obtain the compressed lower envelope LX_inf.

  The cepstral transformation applied to this compressed lower envelope LX_inf is performed according to a frequency scale resulting from a non-linear distortion applied by a module 32. The TFRI module 33 calculates a cepstral vector of NCI cepstral coefficients cx_inf of orders O to NCI-1 representing the lower envelope. NCI is a number that can be

  significantly smaller than NCS, for example NCI = 4.

  The non-linear transformation of the frequency scale for the cepstral transformation of the lower envelope can be performed to a finer scale at the high frequencies than at the low frequencies, which advantageously makes it possible to model well the non-linear components. -voisées signal at high frequencies. However, to ensure homogeneity of representation between the upper envelope and the lower envelope, it may be preferable to adopt in module 32 the same scale as in module 12

(Mel in the example considered).

  The cepstral coefficients cx_inf representing the compressed lower envelope are quantized by a module 34, which can operate in the same way as the module 18 for quantizing the cepstral coefficients representing the compressed upper envelope. In the case considered, where we limit ourselves to NCI = 4 cepstral coefficients for the lower envelope, the vector thus formed is subjected to vector quantization of prediction residue, performed by means identical to those shown in FIG. subdivision into sub-vectors. The quantization index icx = icxi determined by the vector quantizer 22 for each frame relative to the

  coefficients cx_inf is supplied to the output multiplexer 6 of the encoder.

  The encoder shown in FIG. 1 does not include any device

  particular for coding the phases of the spectrum to the harmonics of the audio signal.

  On the other hand, it comprises means 36-40 for encoding a temporal information related to the phase of the non-harmonic component.

  represented by the lower envelope.

  A spectral decompression module 36 and a TFRI module 37

  form a temporal estimate of the non-component

  harmonic. The module 36 applies a reciprocal decompression function of the compression function applied by the module 8 (that is, an exponential or a power function 1 / y) to the compressed lower envelope LXinf produced by the module. interpolation 31. This provides the modulus of the estimated frame of the non-harmonic component, the phase of which is taken equal to that of the spectrum of the signal X on the frame. The inverse Fourier transform performed by the module 37 provides the estimated frame of the

non-harmonic component.

  The module 38 subdivides this estimated frame of the non-component

  harmonic in several time segments. The frame delivered by the module 37 consisting of 2N = 512 weighted samples as illustrated by FIG. 3, the module 38 considers only the N / 2 = 128 first samples -15 - and the N / 2 = 128 last samples, and the subdivide for example into eight segments of 32 consecutive samples each representing 4 ms of signal. For each segment, the module 38 calculates the energy equal to the sum of the squares of the samples, and forms a vector E1 formed of eight positive real components equal to the eight calculated energies. The largest of these eight energies, denoted EM, is also determined to be provided, with the vector E1, to a normalization module 39. This module divides each component of the vector E1 by EM, so that the standardized vector Emix is composed of eight components between 0 and 1. It is this standardized vector Emix, or weighting vector, which is subjected to the quantization by the module 40. It can operate a vector quantization with a dictionary determined during a prior learning . The quantization index iEm is provided by the module 40 to the output multiplexer 6 of the

encoder.

  FIG. 7 shows an alternative embodiment of the means employed by the coder of FIG. 1 for determining the energy-weighting Emix vector of the frame of the non-harmonic component. The spectral decompression modules 36, 37 and TFRI operate as those having the same references in FIG. 1. A selection module 42 is added to determine the value of the spectrum module subjected to the inverse Fourier transform 37. Based on the estimated fundamental frequency FO, the module 42 identifies harmonic regions and non-harmonic regions of the audio signal spectrum. For example, a frequency will be considered as belonging to a harmonic region if it is in a frequency interval centered on a harmonic k.F0 and width

  corresponding to a synthesized spectral line width, and a non-region

  harmonic otherwise. In the non-harmonic regions, the complex signal subjected to the TFRI 37 is equal to the value of the spectrum, that is to say that its modulus and its phase correspond to the values IXI and (px provided by the module of TFR 3 In the harmonic regions, this complex signal has the same phase (Px as the spectrum and a modulus given by the lower envelope after spectral decompression 36. This procedure according to FIG.

  more accurate modeling of non-harmonic regions.

  The decoder shown in FIG. 8 comprises an input demultiplexer 45 which extracts from the bit stream (from an encoder according to FIG. 1) the indices iF, icxs, icxi, iEm of quantization of the fundamental frequency FO, cepstral coefficients representing the compressed upper envelope, coefficients representative of the compressed lower envelope, and the Emix weighting vector, and distributes them respectively to modules 46, 47, 48 and 49. These modules 46-49 comprise dictionaries of similar to those of the modules 5, 18, 34 and 40 of Figure 1, in order to restore the values of the quantized parameters, the modules 47 and 48 have dictionaries for forming the quantized prediction residuals rcxq [n], and deduce the quantified cepstral vectors cxq [n] with elements identical to the elements 23-26 of FIG. 6. These quasified cepstral vectors cx_q [n] provide the cepstral coefficients cxsup_q and cx_ infLq

processed by the decoder.

  A module 51 calculates the fast Fourier transform of the cepstral coefficients cx_sup for each signal frame. The frequency scale of the resulting compressed spectrum is modified non-linearly by a module 52 applying the reciprocal non-linear transformation of that of the module 12 of FIG. 1, and which provides the LX_sup estimate of the compressed upper envelope. . A spectral decompression of LXsup, operated by a module 53, provides the upper envelope Xsup containing the estimated values of the

  multi-frequency spectrum module of the fundamental frequency Fo.

  The module 54 synthesizes the spectral estimation Xv of the harmonic component of the audio signal, by a sum of spectral lines centered on the multiple frequencies of the fundamental frequency F0 and whose

  Amplitudes (in modulus) are those given by the upper envelope X_sup.

  Although the digital input stream O does not have specific information on the phase of the signal spectrum at the harmonics of the fundamental frequency, the decoder of FIG. 8 is able to extract information on this phase from cepstral coefficients cx_sup_q representing the compressed upper envelope. This phase information is used to assign a phase p (k) to each of the spectral lines determined by the

  module 54 in the estimation of the harmonic component of the signal.

  As a first approximation, the speech signal can be considered to be at minimum phase. On the other hand, it is known that minimal phase information can easily be deduced from cepstral modeling. This minimum phase information is therefore calculated for each harmonic frequency. The minimum phase hypothesis means that the energy of the synthesized signal is located at the beginning of each period of the frequency

fundamental Fo.

  To be closer to a real speech signal, a little dispersion is introduced by means of a specific post-ligation of the cepstres during the synthesis of the phase. With this post-stretching, carried out by the module 55 of FIG. 8, it is possible to accentuate the formanic resonances of the envelope and thus to control the dispersion of the phases. This postlifter is for example

of the form (8).

  To limit the phase breaks, it is preferable to smooth the post-lifted cepstral coefficients, which is done by the module 56. The module 57 deduces from the post-lifted cepstral coefficients and smoothed the minimum phase assigned to each spectral line representing a harmonic peak of

spectrum.

  The operations carried out by the minimum phase smoothing and extraction modules 56, 57 are illustrated by the flowchart of FIG. 9. The module 56 examines the variations of the cepstral coefficients to apply a smaller smoothing in the presence of sudden variations. only in the presence of slow variations. For this, he performs the smoothing of the cepstral coefficients by means of a forgetting factor Xc chosen as a function of a comparison between a threshold dth and a distance d between two successive sets of post-lifted cepstral coefficients. The threshold dth is itself adapted according to

  variations of the cepstral coefficients.

  The first step 60 consists in calculating the distance d between the two successive vectors relative to the n-1 and n frames. These vectors, noted here cxp [n-1] and cxp [n], correspond for each frame to the set of NCS post-lifted cepstral coefficients representing the compressed upper envelope. The distance used may notably be the Euclidean distance

  between the two vectors or a quadratic distance.

  Two smoothings are first performed, respectively by means of forgetting factors Xmin and Xmax, to determine a minimum distance dmin and a maximum distance dmax. The threshold dth is then determined in step 70 as being situated between the minimum and maximum distances dmin, dmax:

  dth = 3.dmax + (1-m) .dmin, the coefficient 3 being for example equal to 0.5.

  In the example shown, the forgetting factors Xmin and Xmax are themselves

  the same ones selected from two distinct values, Xmin1, Xmin2 and Xmax1 respectively, Xmax2 lying between 0 and 1, the indices Xmin1, X'max1 each being substantially closer to 0 than the indices Xmin2, Xmax2 'Si d> dmin (test 61) the forgetting factor Xmin is equal to Xmin1 (step 62); otherwise it is taken equal to kmin2 (step 63). In step 64, the minimum distance dmin is taken equal to min.dmin + (1-min) .d. If d> dmax (test 65), the forgetting factor Xmax is equal to Xmaxl (step 66); otherwise it is taken equal to Xmax2 (step 67). In step 68, the minimum distance dmax is taken equal to Xmax dmax + (1-Xmax) .d If the distance d between the two consecutive cepstral vectors is greater than the threshold dth (test 71), then for the forget factor Xc a value Xcl relatively close to 0 (step 72). It is considered in this case that the corresponding signal is of the non-stationary type, so that it is not necessary to keep a large memory of the previous cepstral coefficients. If d <dth, we adopt in step 73 for the forgetting factor Xc a value Xc2 less close to 0 in order to further smooth the cepstral coefficients. The smoothing is performed in step 74, where the vector cx1 [n] of smoothed coefficients for the current frame n is determined by: cxl [n] = Xc.cxl [n-1] + (1-Xc) Cxp [ n] (11) The module 57 then calculates the minimal phases p (k) associated with the harmonics k.F0. In a known way, the minimum phase for a harmonic of order k is given by: NCS-1 qp (k) = - 2. 'cxl [n, m]. sin (27mkF0o / Fe) (12) m = 1

  o cxl [n, m] denotes the smoothed cepstral coefficient of order m for the n-frame.

  In step 75, the harmonic index k is initialized to 1. To initialize the calculation of the minimum phase assigned to the harmonic k, the phase (p (k) and the cepstral index m are initialized respectively at 0 and 1 at step 76. At step 77, the

  module 57 adds to the phase qp (k) the quantity -2.cxl [n, m] .sin (2nmk.F0 / Fe).

  The cepstral index m is incremented in step 78 and compared to NCS in step 79.

  Steps 77 and 78 are repeated as long as m <NCS. When m = NCS, the calculation of the minimum phase is completed for the harmonic k, and the index k is incremented in step 80. The calculation of minimum phases 76-79 is repeated

  for the next harmonic as k.F0 <Fe / 2 (test 81).

  In the exemplary embodiment according to FIG. 8, the module 54 takes into account a constant phase over the width of each spectral line, equal to the minimum phase (p (k) supplied for the corresponding harmonic k by the

module 57.

  The estimate Xv of the harmonic component is synthesized by summation of spectral lines positioned at the harmonic frequencies of the fundamental frequency F0. During this synthesis, it is possible to position the spectral lines on the frequency axis with a resolution greater than the resolution of the Fourier transform. For this, a reference spectral line is precalculated once and for all according to the higher resolution. This calculation can consist of a Fourier transform of the analysis window fA with a transform size of 16384 points, providing a resolution of 0.5 Hz per point. The synthesis of each harmonic line is then performed by the module 54 by positioning the high resolution reference line on the frequency axis, and by sub-sampling this reference spectral line to reduce to the 16.625 Hz resolution of the Fourier transform on

  512 points. This makes it possible to accurately position the spectral line.

  For the determination of the lower envelope, the TFR module 85 of the decoder of FIG. 8 receives the NCI quantized cepstral coefficients cx_inf q from orders 0 to NCI-1, and it advantageously completes them by the NCS-NCI cepstral coefficients cxsup_q from NCI to NCS-1 representing the upper envelope. In fact, it can be estimated as a first approximation that the rapid variations of the compressed lower envelope are well reproduced by those of the compressed upper envelope. In another embodiment, the TFR 85 module could consider only the NCIs

cepstral parameters cxinfq.

  The module 86 converts the frequency scale reciprocally from the conversion operated by the module 32 of the encoder, in order to restore the estimate LXinf of the compressed lower envelope, submitted to the spectral decompression module 87. At the output of the module 87 , the decoder has a lower envelope X_inf comprising the values of the spectrum module

  in the valleys between the harmonic peaks.

  This envelope X_inf will modulate the spectrum of a noise frame whose phase is processed according to the quantified weighting vector Emix extracted by the module 49. A generator 88 delivers a normalized noise frame whose segments of 4 ms are weighted in a module 89 according to the standardized components of the Emix vector provided by the module 49 for the current frame. This noise is a high-pass filtered white noise to account for the low level that normally has the unvoiced component at low frequencies. From the energy-weighted noise, the module 90 forms frames of 2N = 512 samples by applying the analysis window fA, the insertion of 256 samples at zero and the circular permutation for the phase compensation according to what has been done. The Fourier transform of the resulting frame is computed by the TFR module 91. The spectral estimation Xuv of the non-harmonic component is determined by the spectral synthesis module 92 which performs a frequency weighting. by frequency. This weighting consists of multiplying each complex spectral value provided by the TFR module 91 by the value of the lower envelope X_inf obtained for the same frequency by the

  spectral decompression module 87.

  The spectral estimates Xv, Xuv of the harmonic (voiced in the case of a speech signal) and non-harmonic (or non-voiced) components are combined by a mixing module 95 controlled by a module 96

  analyzing the degree of harmonicity (or voicing) of the signal.

  The organization of these modules 95, 96 is illustrated in FIG. 10. The analysis module 96 comprises a unit 97 for estimating a degree of voicing W depending on the frequency, from which four gains depending on the frequency are calculated. the frequency, namely two gains gv, guv controlling the relative importance of the harmonic and nonharmonic components in the synthesized signal, and two gains gv_, gLuv _p used to noise the phase of the

harmonic component.

  The degree of voicing W (i) is a continuously varying value between 0 and 1 determined for each frequency index i (0 <i <N) as a function of the upper envelope Xsup (i) and the lower envelope 21 - Xinf (i) obtained for this frequency i by the decompression modules 53, 87. The degree of voicing W (i) is estimated by the unit 97 for each index of frequency i corresponding to a harmonic of the fundamental frequency Fo i = 2NkF + i for k = 1,2, ..., by an increasing function of the Fe2 ratio between the upper envelope X_sup and the lower envelope Xinf at this frequency, for example according to the formula W (i ) -min, 1 {10. log0 [X_suP (i) / Xinf (i)] (13) W (i) - min 1, Vt __ (F0 1 3 The threshold Vth (F0) corresponds to the average dynamic calculated on a synthetic spectrum purely voiced at the frequency It is essential

  advantageously chosen depending on the fundamental frequency F0.

  The degree of voicing W (i) for a frequency other than the harmonic frequencies is obtained simply as being equal to that

  estimated for the nearest harmonic.

  The gain gv (i), which depends on the frequency, is obtained by applying a

  i5 non-linear function at the degree of voicing W (i) (block 98). This non-function

  For example, the linear form has the form shown in FIG. 11i gv (i) = 0 if 0 W (i) W1 gV (i) - () W1 siW1 <W (i) <W2 (14)

W2- W1 (

  gv (i) = 1 if W2 <W (i) <1 the thresholds Wl, W2 being such that O <Wl <W2 <1. The guv gain can be calculated in a similar way to the gain gv (the sum of the two gains gv , guv being constant, for example equal to 1), or simply deduced from it by the relation guv (i) = 1 - gv (i), as schematized by the subtractor 99 on the

figure 10.

  It is interesting to be able to noise the phase of the harmonic component of the signal at a given frequency if the analysis of the degree of voicing shows that the signal is rather non-harmonic type at this frequency. For this, the phase (Pv of the mixed harmonic component is the result of a linear combination of the phases (pv, (Puv of the components

  harmonic and non-harmonic Xv, Xuv synthesized by the modules 54, 92.

  The gains gv_ (p guv_p respectively applied to these phases are calculated from the degree of voicing W and weighted also according to the frequency index i, since the sounding of the phase is not really true.

  useful only beyond a certain frequency.

  A first gain gv1 _ is calculated by applying a non-linear function to the degree of voicing W (i), as shown schematically by block 100 in FIG. 10. This non-linear function can have the form shown in FIG. 12: gvlq (i) = G1 if 0 <W (i) <W3 gv1 (i) = Gl + (l-G1) W (i) - W3 if W3 <W (i) <W4 (15) W4-W3 gv1 (i) = 1 if W4 <W (i) <1 the thresholds W3 and W4 being such that 0 <W3 <W4 <1, and the minimum gain G1

being between 0 and 1.

  A multiplier 101 multiplies for each index frequency i the gain gv1_ by another gain gv2_, p depending only on the frequency index i, to form the gain gv_ (i). The gain gv2, p (i) depends non-linearly on the frequency index i, for example as shown in FIG. 13 gv2_e (i) = 1 if 0 <i <il gv2 _, (i) = 1- (1) -G2) it if it <i <i2 (16) gv2_ (p i2 -it gv2_p (i) = G2 if i2 <i <1 the indexes il and i2 being such that 0 <il <i2 <N, and the gain with G2 being between 0 and 1. The gain guv p (i) can be calculated simply as being equal to 1 - gv p (i) = 1 - gv1 (i) .gv2_e (i) (subtracter 102 of the figure) The complex spectrum Y of the synthesized signal is produced by the mixing module 95, which carries out the following mixing relation, for 0 <i <N: Y (i) = gv (i) Xv (i) I. expuj (Pv (i)] + guv (i) Xuv (i) (17) with (Pv (i) = gv _ (i). (PV (i) + guv (i). (Puv (i) (18)) (pv (i) designates the argument of the complex number Xv (i) supplied by the module 54 for the index frequency i (block 104 of FIG. 10), and (puv (i) designates

- 23 -

  The argument of the complex number Xuv (i) provided by the module 92 (block 105 of FIG. 10). This combination is achieved by the multipliers 106110 and the

  adders 111-112 shown in FIG.

  The mixed spectrum Y (i) for 0 - i <2N (with Y (2N-1-i) = Y (i)) is then transformed in the time domain by the TFRI module 115 (FIG. 8). Only the first N / 2 = 128 and the N / 2 = last 128 samples of the frame of 2N = 512 samples produced by the module 115 are retained, and the inverse circular permutation of that illustrated in FIG. the synthesized frame of N = 256 samples weighted by the analysis window fA 'The frames successively obtained in this manner are finally processed by the temporal synthesis module 116 which forms the audio signal

decoded k.

  The time synthesis module 116 performs an overlapping sum of frames modified with respect to those successively evaluated at the output of the module 115. The modification can be seen in two

  steps illustrated respectively in Figures 14 and 15.

  The first step (FIG. 14) consists in multiplying each frame 2 'delivered by the TFRI module 115 by a window 1 / fA inverse to the analysis window fA used by the module 1 of the encoder. The samples of the frame

  2 "that result are therefore weighted uniformly.

  The second step (FIG. 15) consists in multiplying the samples of this frame 2 "by a synthesis window fs satisfying the following properties: fs (N-L + i) + fs (i) = A for O <i <L ( 19) fs (i) = A for L i <NL (20) where A denotes an arbitrary positive constant, for example A = 1. The synthesis window fs (i) progressively increases from 0 to A for i ranging from 0 to L. For example, a raised half-sinusoid: fs (i) = A. (1-cos [(i + 112) 7 / LD for 0 <i <L (21) After reweighting each 2 "frame by the synthesis window fs, the module 116 positions the successive frames with their time offsets of M = 160 samples and their temporal overlaps of L = 96 samples, then it performs the sum of the frames thus positioned in time. 19) and (20) of the synthesis window fs, each sample of the decoded audio signal k thus obtained is assigned a uniform overall weight, equal to A. This overall weight l comes from the contribution of a single frame if the sample has in this frame a rank i such that L <i <N - L, and comprises the summed contributions of two successive frames if 0 <i <L o N - L <i <N. It is thus possible to perform the temporal synthesis in a simple manner even if, as in the case considered, the overlap L between two frames

  successive is smaller than half the size N of these frames.

  The two steps outlined above for modifying the signal frames can be merged in one step. It suffices to precompute a composite window fc (i) = fs (i) / fA (i), and to simply multiply the frames 2 'of N = 256 samples delivered by the module 115 by the composite window

  fc before performing the overlay summation.

  FIG. 16 shows the appearance of the composite window fc in the case where the analysis window fA is a Hamming window and the synthesis window fs

  the form given by relations (19) to (21).

  Other forms of the synthesis window fs satisfying relationships (19) and (20) can be employed. In the variant of FIG. 17, it is a piecewise affine function defined by: fs (i) = Ai / L for 0 <i <L (22) In order to improve the coding quality of the audio signal, the coder of FIG. 1 can increase the rate of frame formation and analysis, in order to transmit more quantization parameters to the decoder. In the frame structure shown in Fig. 2, a frame of N = 256 samples (32 ms) is formed every 20 ms. These frames of 256 samples could be formed at a higher rate, for example 10 ms, two successive frames then having an offset of M / 2 = 80

  samples and an overlay of 176 samples.

  In these conditions, it is possible to transmit the complete sets of

  quantization parameters iF, icxs, icxi, iEm for only one sub-

  set of frames, and transmit for the other frames parameters to perform adequate interpolation at the decoder. In the example envisaged above, the subset for which complete sets of parameters are transmitted can be constituted by the frames of rank -25 - integer n, whose periodicity is M / Fe = 20 ms, and the frames for which interpolation is performed can be half-integer n + 1/2

  which are shifted by 10 ms from the frames of the subassembly.

  In the embodiment illustrated in FIG. 18, the notations cxq [n-1] and cxq [n] denote determined quasified cepstral vectors, for two successive frames of entire rank, by the quantization module 18 and / or by the module of Quantization 34. These vectors comprise for example four consecutive cepstral coefficients each. They could also

  understand more cepstral coefficients.

  A module 120 interpolates these two cepstral vectors cxq [n-1] and cxq [n], in order to estimate an intermediate value cxi [n-1/2]. The interpolation performed by the module 120 can be a simple arithmetic mean of the vectors cxq [n-1] and cxq [n]. As a variant, the module 120 could apply a more sophisticated interpolation formula, for example polynomial, also based on the cepstral vectors obtained for frames prior to the n-1 frame. On the other hand, if more than one interpolated frame is interposed between two consecutive frames of full rank,

  the interpolation takes into account the relative position of each interpolated frame.

  Using the means previously described, the coder calculates

  also the cepstral coefficients cx [n-1/2] relating to the half-rank weft

  whole. In the case of the upper envelope, these cepstral coefficients are those provided by the TFRI module 13 after post-scaling (for example with the same post-scaling coefficients as for the previous frame n-1) and normalization 16. In the case of the lower envelope, the coefficients

  cepstral cx [n-1/2] are those delivered by the TFRI 33 module.

  A subtractor 121 forms the difference ecx [n-1/2] between the cepstral coefficients cx [n-1/2] calculated for the half-integer field and the coefficients cxi [n-1/2] estimated by interpolation. This difference is provided to a quantization module 122 which addresses quantization indices icx [n-1/2] to the output multiplexer 6 of the encoder. The module 122 operates for example by vector quantization interpolation errors

  ecx [n-1/2] successively determined for frames of half-integer rank.

  This quantization of the interpolation error can be carried out by the coder for each of the NCS + NCI coefficients used by the decoder, or only for some of them, typically those of orders

- 26 -

the little ones.

  The corresponding means of the decoder are illustrated in FIG. 19.

  The decoder operates essentially as described with reference to FIG. 8 to determine the full rank signal frames. An interpolation module 124 identical to the encoder module 120 estimates the intermediate coefficients cxi [n-112] from the quantized coefficients cxq [n-1] and cxq [n] provided by the module 47 and / or the module 48 to from the indexes icxs, icxi extracted from the stream t. A parameter extraction module 125 receives the quantization index icx [n-1/2] from the input demultiplexer 45 of the decoder, and deduces therefrom the quantized interpolation error ecx_q [n-1/2] from the same quantization dictionary as used by the encoder module 122. An adder 126 sums the cepstral vectors cx_i [n-1/2] and ecxq [n-1/2] in order to provide the cepstral coefficients cx [n-1/2] which will be used by the decoder (modules 51- 57, 95, 96, 115 and / or modules 85-87, 92,

* 95, 96, 115) to form the interpolated frame of rank n-1/2.

  If only some of the cepstral coefficients have been quantized for interpolation error, the others are determined by the

  decoder by simple interpolation, without correction.

  The decoder can also interpolate the other parameters F0, Emix used to synthesize the signal frames. The fundamental frequency F0 can be interpolated linearly, either in the time domain or (preferably) directly in the frequency domain. For the possible interpolation of the Emix energy weighting vector, the interpolation after denormalization should be carried out, taking into account, of course, the

time delays between frames.

  It should be noted that it is particularly advantageous, to interpolate the representation of the spectral envelopes, to perform this interpolation in the cepstral domain. Unlike interpolation performed on other parameters, such as LSP ("Line Spectrum Pairs") coefficients, the linear interpolation of the cepstral coefficients corresponds to the interpolation

  linear spectral amplitudes compressed.

  In the variant shown in FIG. 20, the coder uses the cepstral vectors cx_q [n], cxq [n-1], ..., cx_q [nr] and cxq [n-1/2] calculated for the last past frames. (r> 1) to identify an optimal interpolator filter which, when subjected to quantized cepstral vectors cxq [nr], -27 - cx_q [n] relative to the whole rank frames, delivers an interpolated cepstral vector cxi [n- 1/2] which has a minimal distance with the vector

  cx [n-1/2] calculated for the last frame of half-integer rank.

  In the example shown in FIG. 20, this interpolator filter 128 is present in the encoder, and a subtractor 129 subtracts its output cx_i [n-112] from the calculated cepstral vector cx [n-112]. A minimization module determines the set of parameters {P} of the interpolator filter 128, for which the ecx interpolation error [n-1/2] delivered by the subtractor 129 has a minimum standard. This parameter set {P} is addressed to a quantization module 131 which provides a quantization index corresponding to iP to

output multiplexer 6 of the encoder.

  As a function of the flow rate allocated in the stream c to the quantization indexes of the parameters {P} defining the optimal interpolator filter 128, it will be possible to adopt a more or less fine quantization of these parameters, or a more or less elaborate form of the interpolator filter, or still provide several interpolator filters quantized separately for different vectors of

cepstral coefficients.

  In a simple embodiment, the interpolator filter 128 is linear, with r = 1: cxi [n-1/12] = p.cx_q [n-1] + (1-p) .cx_q [n] (23)

  and the parameter set {P} is limited to the coefficient p between 0 and 1.

  From the quantization iP indexes of the parameters {P} obtained in the bit stream (p, the decoder reconstructs the interpolator filter 128 (with quantization errors close), and processes the spectral vectors cx _q [nr], ..., cxq [n] to estimate the cepstral coefficients cx [n-1/2] used to synthesize the

frames of half-whole rank.

  In general, the decoder can use a simple interpolation method (without transmission of parameters by the coder for half-integer frames), an interpolation method taking into account an interpolation error. quantized (according to FIGS. 17 and 18), or an interpolation method with an optimal interpolator filter (according to FIG. 19) for evaluating the half-integer rank frames in addition to the rank frames

  integer evaluated directly as explained with reference to FIGS. 8-13.

  The temporal synthesis module 116 can then combine all of these

  frames evaluated to form the synthesized signal x in the manner explained above.

-28 -

  after referring to Figures 14, 21 and 22.

  As in the temporal synthesis method previously described, the module 116 performs an overlap sum of frames modified with respect to those successively evaluated at the output of the module 115, and this modification can be seen in two steps, the first of which is identical to that previously described with reference to Figure 14 (divide

  samples of the 2 'frame by the analysis window fA).

  The second step (FIG. 21) consists in multiplying the samples of the renormalized frame 2 "by a synthesis window fs satisfying the following properties: fs (i) = 0 for 0 <i <N / 2-M / p and N / 2 + M / p <i <N (24) fs (i) + fs (i + M / p) = A for N / 2 - M / p <i <N / 2 (25) where A denotes a positive constant arbitrary, for example A = 1, and p is the integer such that the time offset between the successive frames (calculated directly and interpolated) is M / p samples, or p = 2 in the example described. fs (i) increases gradually for i ranging from N / 2 - M / p to N / 2, for example a raised sinusoid over the N / 2 - M / p <i <N / 2 + M / interval p In particular, the synthesis window fs may be, over this interval, a Hamming window (as shown in FIG.

or a Hanning window.

  FIG. 21 shows the successive frames 2 "repositioned in time by the module 116. The hatches indicate the portions removed from the frames (synthesis window at 0. It can be seen that by performing the overlap sum of the samples of the successive frames, the property (25)

  ensures a homogeneous weighting of the samples of the synthesized signal.

  As in the synthesis method illustrated in FIGS. 14 and 15, the weighting procedure of the Fourier transform inverse fields of the Y spectra can be carried out in a single step, with a composite window fc (i) = fs (i) / fA (i). Figure 22 shows the shape of the window

  composed fc in the case where the windows fA and fs are of type Hamming.

  As the temporal synthesis method illustrated in FIGS. 14 to 17, that illustrated by FIGS. 14, 21 and 22 makes it possible to take into account an overlap L between two analysis frames (for which the analysis is carried out of complete way) smaller than half than the size N of these frames. In general, the latter method is applicable when the successive analysis frames have mutual time offsets M of more than N / 2 samples (even possibly more than N samples if a very low flow rate is required), the interpolation leading to to a set of frames whose mutual time offsets are less than N / 2 samples. Interpolated frames may be subject to reduced transmission of coding parameters, as previously described, but this is not required. This embodiment makes it possible to maintain a relatively large interval M between two analysis frames, and thus to limit the required transmission rate, while limiting the discontinuities that may appear due to the size of this interval relative to the scales. typical times of variations in the parameters of the audio signal, including

  cepstral coefficients and the fundamental frequency.

-30 -

Claims (17)

  A method of decoding an input digital stream (1) representing an encoded audio signal, in which a set of successive frames of N audio signal samples is synthesized from encoding data included in the digital stream of input, in which the coding data comprise, for only a subset of the frames, data representative of spectral amplitudes associated with frequencies of the spectrum of the audio signal, characterized in that for each of the frames of said sub-frame are determined together, on the basis of the encoding data, cepstral coefficients representative of at least some of said spectral amplitudes, and in that for frames not forming part of said subset, said cepstral coefficients are interpolated, and generated at the same time; using interpolated cepstral coefficients a spectral estimate (Y) of the audio signal   transforms in the time domain to obtain the synthesized frame.   The method of claim 1, wherein the encoding data comprises quantization data of said cepstral coefficients. The method according to claim 1 or 2, wherein a fundamental frequency (F0) of the audio signal is determined from quantization data included in the input bit stream (ó), wherein for each frame of said subset from the encoding data, an upper spectral envelope (Xsup) of the audio signal corresponding to spectral amplitudes associated with multiple frequencies of the fundamental frequency, and in which for each frame not belonging to said subset, is determined from the encoding data. the spectral envelope is determined   of the audio signal from the interpolated cepstral coefficients.   4. Method according to any one of claims 1 to 3, in   wherein a fundamental frequency (F0) of the audio signal is determined from quantization data included in the input bit stream (X), wherein for each frame of said subset, from the encoding data is determined, a lower spectral envelope (X_inf) of the audio signal corresponding to spectral amplitudes associated with frequencies located in areas of the intermediate spectrum with respect to the multiple frequencies of the fundamental frequency, and wherein for each frame not forming part of said subassembly, the lower spectral envelope of the   audio signal from the interpolated cepstral coefficients.   The method according to any one of claims 1 to 4,   wherein the successive frames of said set are overlapping and composed of N samples of the audio signal weighted by an analysis window (fA), in which the successive frames of said subset have mutual time offsets of M samples, the number M being more large than N / 2, while the successive frames of said set have mutual time offsets of M / p samples, p being an integer greater than 1, in which each synthesized frame of the set is modified by applying to it a corresponding processing division by said analysis window (fA) and multiplication by a synthesis window (fs), and forming the decoded audio signal (x) as an overlap sum of the modified frames, and wherein, the samples of a frame having ranks i numbered from 0 to N-1, the synthesis window fs (i) has a support limited to ranks i ranging from N / 2 - M / p to N / 2 + M / p and verifies fs (i) + fs (i + M / p ) = A for   N / 2 - M / p <i <N / 2, where A is a positive constant.   6. Process according to any one of claims 1 to 5, in   which, for frames not belonging to said subset, corrects the interpolated cepstral coefficients (cxi [n-1/2]) on the basis of error quantization data (icx [n-1/2]) Interpolation (ecxq [n1 / 2]) included in the coding data.   The process according to any one of claims 1 to 5, wherein   which, for frames not belonging to said subset, interpolates the cepstral coefficients (cx_q [n]) by a filter (128) determined on interpolator filter quantization data (iP) included in the data coding.   Audio decoder, comprising means for executing a method   according to any one of claims 1 to 7.   -32 - A method of coding an audio signal (x), wherein a spectrum of the audio signal is determined by a frequency domain transform of a frame of the audio signal, and included in a digital output stream (c) data representative of spectral amplitudes associated with at least some of the frequencies of the spectrum, in which the spectrum of the audio signal is determined for a set of successive frames of N samples of the audio signal, and in which for each frame of said set is determined cepstral coefficients representative of at least some of said spectral amplitudes, characterized in that said data representative of the spectral amplitudes are included in the output digital stream for only a subset of the frames, and in that for the frames which are not part of of said subset, data (icx [n-1/2]) for quantizing an error is included in the output digital flow (O).   interpolation (ecx [n-112]) of said cepstral coefficients.   A method of encoding an audio signal (x), wherein a spectrum of the audio signal is determined by a frequency domain transform of a frame of the audio signal, and included in a digital output stream (O) data representative of spectral amplitudes associated with at least some of the frequencies of the spectrum, in which the spectrum of the audio signal is determined for a set of successive frames of N samples of the audio signal, and in which for each frame of said set is determined cepstral coefficients representative of at least some of said spectral amplitudes, characterized in that said data representative of the spectral amplitudes are included in the output digital stream for only a subset of the frames, and in that for the frames which are not part of of said subassembly, an optimum interpolator (128) is determined for said cepstral coefficients, and included in the digital output (O) data (iP) representing said filter optimal interpolator.   The method of claim 9 or 10, wherein said data representative of the spectral amplitudes comprise data   of quantification of cepstral coefficients. -33 -   The method of any one of claims 9 to 11, wherein   which estimates a fundamental frequency (Fo) of the audio signal, and wherein the interpolated cepstral coefficients comprise cepstral coefficients calculated by transforming in the cepstral domain a compressed upper envelope (LXsup) of the audio signal spectrum. The method of claim 12, wherein the compressed upper envelope (LXsup) is determined by interpolation of spectral amplitudes associated with multiple frequencies of the fundamental frequency.   (Fo), with application of a spectral compression function.   14. Process according to any one of Claims 9 to 13, in   which estimates a fundamental frequency (F0) of the audio signal, and wherein the interpolated cepstral coefficients comprise cepstral coefficients calculated by transforming in the cepstral domain an envelope   lower compressed (LXinf) of the audio signal spectrum.   The method of claim 14, wherein the compressed lower envelope (LXinf) is determined by interpolating spectral amplitudes associated with frequencies in intermediate spectrum areas with respect to the frequency multiples of the frequency.   fundamental (F0), with application of a spectral compression function.   16. The method according to any one of claims 9 to 15, in   wherein the successive frames of said subset have mutual time offsets of more than N / 2 samples, and the successive frames of said set have mutual time offsets of less than N / 2 samples. 17. Audio coder, comprising means for executing a method   according to any one of claims 9 to 16.