EP2652735B1 - Verbesserte kodierung einer verbesserungsstufe bei einem hierarchischen kodierer - Google Patents

Verbesserte kodierung einer verbesserungsstufe bei einem hierarchischen kodierer Download PDF

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EP2652735B1
EP2652735B1 EP11811097.2A EP11811097A EP2652735B1 EP 2652735 B1 EP2652735 B1 EP 2652735B1 EP 11811097 A EP11811097 A EP 11811097A EP 2652735 B1 EP2652735 B1 EP 2652735B1
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stage
coding
signal
quantization
improvement
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French (fr)
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EP2652735A1 (de
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Balazs Kovesi
Stéphane RAGOT
Alain Le Guyader
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Orange SA
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Orange SA
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/06—Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
    • G10L19/0204—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders using subband decomposition
    • G10L19/0208—Subband vocoders
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
    • G10L19/032—Quantisation or dequantisation of spectral components
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/16—Vocoder architecture
    • G10L19/18—Vocoders using multiple modes
    • G10L19/24—Variable rate codecs, e.g. for generating different qualities using a scalable representation such as hierarchical encoding or layered encoding

Definitions

  • the present invention relates to the field of coding digital signals.
  • the coding according to the invention is particularly suitable for the transmission and / or storage of digital signals such as audio-frequency signals (speech, music or other).
  • the present invention relates more particularly to the coding of waveforms such as the coding MIC (for "Coded Pulse Modulation") said PCM (for "Pulse Code Modulation”) in English, or the adaptive coding of waveform of the ADPCM encoding type (for "Adaptive Differential Pulse Code Modulation” ) in which the invention relates in particular to embedded code coding for issuing indexes of Scalable bit stream quantization.
  • the coding MIC for "Coded Pulse Modulation”
  • PCM for "Pulse Code Modulation”
  • ADPCM encoding type for "Adaptive Differential Pulse Code Modulation”
  • ITU-T Recommendation G.722 or ITU-T G.727 The general principle of nested code ADPCM coding / decoding specified by ITU-T Recommendation G.722 or ITU-T G.727 is as described with reference to figures 1 and 2 .
  • the quantization index I B + K ( n ) of B + K bits at the output of the quantization module Q B + K is transmitted via the transmission channel 140 to the decoder as described with reference to FIG. figure 2 .
  • the dashed portion referenced 155 represents the low rate local decoder which contains the predictors 165 and 175 and the inverse quantizer 121.
  • This local decoder thus makes it possible to adapt the inverse quantizer at 170 from the low bit rate index I B ( n ) and to adapt the predictors 165 and 175 from the reconstructed low bit rate data.
  • the symbol "'" indicates a decoded value from the received bits, possibly different from that used by the encoder due to transmission errors.
  • the output signal r ' B ( n ) for B bits will be equal to the sum of the signal prediction and the output of the B-bit inverse quantizer.
  • This part 255 of the decoder is identical to the local low speed decoder 155 of the figure 1 .
  • the decoder can improve the restored signal.
  • the output will be equal to the sum of the prediction x P B not and from the output of the inverse quantizer 230 to B + 1 bits there I B + 1 'B + 1 not ⁇ V' not .
  • ITU-T G.722 nested code ADPCM (hereinafter referred to as G.722) coding broadband signals which are defined with a minimum bandwidth of [50-7000 Hz] and sampled at 16 kHz.
  • the G.722 encoding is an ADPCM coding of each of the two sub-bands of the signal [0-4000 Hz] and [4000-8000 Hz] obtained by decomposition of the signal by quadrature mirror filters.
  • the low band is coded by a 6, 5 and 4 bit nested code ADPCM coding while the high band is coded by a 2 bit ADPCM coder per sample.
  • the total bit rate will be 64, 56 or 48 bit / s depending on the number of bits used for decoding the low band.
  • This coding was first developed for use in ISDN (Digital Integrated Services Network). It has recently been deployed in high quality voice over IP telephony applications.
  • the quantization noise spectrum will be relatively flat.
  • the noise may have a comparable level or higher than the signal and is therefore not necessarily masked. It can then become audible in these regions.
  • Coding noise formatting is therefore necessary.
  • coding noise formatting suitable for nested code encoding is furthermore desirable.
  • the purpose of the formatting of the coding noise is to obtain a quantization noise whose spectral envelope follows the short-term masking threshold; this principle is often simplified so that the noise spectrum follows the signal spectrum approximately, providing a more homogeneous signal-to-noise ratio so that the noise remains inaudible even in the lower energy areas of the signal.
  • G.711.1 Wideband embedded extension for G.711 pulse code modulation
  • G.711.1 A wideband extension to ITU-T G.711.
  • This recommendation thus describes coding with coding noise formatting for heart rate coding.
  • a perceptual filter for shaping the coding noise is calculated based on the decoded past signals from a reverse core quantizer.
  • a local heart rate decoder thus makes it possible to calculate the noise shaping filter.
  • this noise shaping filter is possible to calculate from decoded heart rate signals.
  • a quantizer delivering improvement bits is used at the encoder.
  • the decoder receiving the core bit stream and the improvement bits, calculates the coding noise shaping filter in the same way as the coder from the decoded heart rate signal and applies this filter to the output signal of the decoder.
  • inverse quantizer of the enhancement bits the shaped high-speed signal being obtained by adding the filtered signal to the decoded heart signal.
  • the shaping of the noise thus improves the perceptual quality of the heart rate signal. It offers a limited improvement in quality for improvement bits. Indeed, the formatting of the coding noise is not carried out for the coding of the improvement bits, the input of the quantizer being the same for the quantization of the core as for the improved quantization.
  • the decoder must then remove a resulting parasitic component by a matched filtering, when the improvement bits are decoded in addition to the core bits.
  • quantization is performed by minimizing a quadratic error criterion in a perceptually filtered domain.
  • a coding noise shaping filter is defined and applied to a given error signal from at least one reconstructed signal of a preceding coding stage.
  • the method also requires the calculation of the reconstructed signal of the current improvement stage in anticipation of a next coding stage.
  • improvement terms are calculated and stored for the current improvement stage. This therefore brings significant complexity and significant storage enhancement terms or reconstructed signal samples of previous stages.
  • the present invention improves the situation.
  • the quantization of the improvement stage determines the quantization index bit or bits which are directly concatenated with the indices of the preceding stages. Unlike the state-of-the-art methods, there is no computation of an improvement signal or improvement terms.
  • the input signal of the quantization is either directly the input signal of the hierarchical coder, or the same input signal having directly undergone perceptual weighting processing. This is not a signal difference between the input signal and a reconstructed signal of the previous coding stages as in the techniques of the state of the art.
  • stored quantization values are not differential values. Thus, it is not useful to memorize the quantization values used for reconstruction in the previous stages to form a quantization dictionary of the improvement stage.
  • the invention avoids the duplication of the dictionaries that can be encountered in the methods of the state of the art where a differential dictionary is used at the encoder and an absolute dictionary at the decoder.
  • the memory required for the storage of the dictionaries and the quantification operations at the encoder and inverse quantization at the decoder is therefore reduced.
  • the input signal has undergone perceptual weighting processing using a predetermined weighting filter to provide a modified input signal, prior to the quantization step, and the method further includes a step of adapting the weighting filter memories from the quantized signal of the current enhancement coding stage.
  • This perceptual weighting processing applied directly to the input signal of the hierarchical coder for the enhancement coding of the stage k also reduces the complexity in terms of computational load compared to state-of-the-art techniques which performed this perceptual weighting processing on a difference signal between the input signal and a reconstructed signal of the previous coding stages.
  • the encoding method described also allows existing decoders to decode the signal without having to make any additional modifications or processing to be expected while benefiting from the improvement of the signal by formatting the effective coding noise.
  • the possible quantization values for the improvement stage k further contain a scale factor and a prediction value from the adaptive type core coding.
  • the modified input signal to be quantized at the improvement stage k is the perceptually weighted input signal from which a prediction value derived from the adaptive type core coding is subtracted.
  • the perceptual weighting treatment is performed by prediction filters forming an ARMA type filter.
  • the hierarchical coder further comprises a perceptual weighting pre-processing module using a predetermined weighting filter to give a modified input signal of the quantization module and a weighting filter memory adaptation module from the quantized signal. of the current improvement coding stage.
  • the hierarchical coder provides the same advantages as those of the method it implements.
  • It also relates to a computer program comprising code instructions for implementing the steps of the encoding method according to the invention, when these instructions are executed by a processor.
  • the invention finally relates to a storage means readable by a processor storing a computer program as described.
  • the improvement stage (of rank k) is presented as producing one additional bit per sample.
  • the coding in each improvement stage involves selecting one of two possible values.
  • the "absolute dictionary" - in terms of absolute levels (in the sense of "non-differential") - corresponding to all the quantization values that can be produced by the rank improvement stage k is of size 2 B + k , or sometimes slightly less than 2 B + k as for example in the G.722 coder which has only 60 possible levels instead of 64 in the quantizer of 6 bits of low band.
  • Hierarchical coding implies a binary tree structure of the "absolute dictionary", which explains why it suffices to have an improvement bit to perform the coding given the B + k-1 bits of the preceding stages.
  • the duplication of the reconstruction levels is in fact a consequence of the low band hierarchical coding constraint which is implemented in G.722 in the form of a scalar quantization dictionary (at 4, 5 or 6 bits per sample ) structured in a tree.
  • the coding of the improvement stage according to the invention is very easily generalizable for cases where the improvement stage adds several bits per sample.
  • the size of the dictionary D k (n) used in the improvement stage, as defined later, is simply 2 U where U> 1 is the number of bits per sample of the improvement stage.
  • the encoder as represented in figure 3 shows a nested coder or hierarchical coder in which a B-bit core coding and at least one rank improvement stage k is provided.
  • the core coding and the improvement stages preceding the coding of the improvement stage k as represented at 306, deliver scalar quantization indices which are concatenated to form the indices of the preceding nested encoder I B + k-1 ( not).
  • the figure 3 simply illustrates a PCM / ADPCM coding module 302 representing the nested coding preceding the enhancement coding at 306.
  • the core encoding of the preceding nested encoding can optionally be performed using the masking filter determined at 301 to format "core" coding noise.
  • An example of this type of core coding is described later with reference to the figure 8 .
  • This module 302 thus delivers the indices I B + k-1 (n) of the nested encoder as well as the prediction signal x P B not and the scale factor v (n) in the case where it is indeed a predictive coding ADPCM similar to that described with reference to the figure 1 .
  • the module 302 simply delivers the nested quantization indices I B + k-1 (n).
  • the "absolute dictionary” is a dictionary structured in tree. The index I B + k -1 conditions the different branches of the tree to be taken into account in order to determine the possible quantization values of the stage k (D k (n)).
  • the scaling factor v (n) is determined by the core stage of the ADPCM coding as illustrated in FIG. figure 1 , the improvement stage therefore uses this same scale factor to scale the codewords of the quantization dictionary.
  • the coder of the figure 3 does not include the modules 301 and 310, that is to say that there is no provision for encoding noise shaping processing. Thus, it is the input signal x (n) itself that is quantized by the quantization module 306.
  • the encoder further comprises a module 301 for calculating a masking filter and for determining the weighting filter W (z) or a predictive version W PRED (z) described later.
  • the masking or weighting filter is determined here from the input signal x (n) but could very well be determined from a decoded signal, for example from the decoded signal of the preceding nested encoder x B + k-1 ( n ) .
  • the masking filter can be determined or adapted sample by sample or by block of samples.
  • the encoder according to the invention performs a shaping of the coding noise of the improvement stage by using a quantization in the domain weighted by the filter W (z), that is to say by minimizing the quantization noise energy filtered by W (z).
  • This weighting filter is used at 311 by the filtering module and more generally by the perceptual weighting module 310 of the input signal x (n). This pretreatment is applied directly to the input signal x (n) and not to an error signal as could be the case in state-of-the-art techniques.
  • This pretreatment module 310 delivers a modified signal x '(n) at the input of the enhancement quantizer 307.
  • the quantization module 307 of the improvement stage k delivers a quantization index I in h B + k (n) which will be concatenated with the indices of the preceding nested encoding (I B + k-1 ) to form the nested encoding indices. current (I B + k ), by a module not shown here.
  • the quantization module 307 of the improvement stage k chooses between the two values d 1 B + k not and d 2 B + k not of the adaptive dictionary D k (n).
  • the module 308 gives the quantized value of the input signal by inverse quantization of the index I enh B + k not .
  • This quantized signal is used to update the memories of the weighting filter W (z) of the enhancement stage to obtain memories corresponding to an input x ( n ) -x B + k ( n ) .
  • W (z) weighting filter W (z) of the enhancement stage.
  • the quantization of the signal x (n) is done in the weighted domain, which means that we minimize the squared error between x ( n ) and x B + k ( n ) after filtering by the filter W ( z ).
  • the quantization noise of the enhancement stage is thus shaped by a 1 / W (z) filter to make this noise less audible. The energy of the weighted quantization noise is thus minimized.
  • the general embodiment of the block 310 given on the figure 3 shows the general case where W (z) is an infinite impulse response (IIR) filter or a finite impulse response (FIR) filter.
  • the signal x ' ( n ) is obtained by filtering x ( n ) by W ( z ) and then when the quantified value x B + k (n) is known, the memories of the filter W ( z ) are updated as if the filtering had been done on the signal x ( n ) -x B + k ( n ) .
  • the dotted arrow represents the update of the filter memories.
  • the input signal has undergone perceptual weighting processing by using a predetermined weighting filter at 301 to give a modified input signal x '(n), before the quantization step at 306.
  • the figure 3 also represents the step of adapting the weighting filter memories 311 to the quantized signal ( x B + k ( n )) of the current enhancement coding stage.
  • FIR finite impulse response
  • N D being the order of the perceptual filter W ( z ).
  • the input signal x ( n ) is encoded by the MIC / ADPCM coding module 302, with or without shaping the coding noise of the nested encoder B + k-1,
  • an adaptive dictionary D k is constructed according to the prediction values x P B not , the scaling factor v ( n ) of the heart stage in the case of an ADPCM type adaptive coding and I B + k-1 ( n ) coding indices as explained with reference to FIG. figure 3 .
  • H PRED (z) denotes a filter whose coefficient for its current input x ( n ) is zero.
  • All-pole recursive filters 1 B z or ARMA AT z B z are the so-called TIR filters for Infinite Impulse Response in English ( Infinite Impulse Response Filter).
  • figure 4 using the filtering of a filtering into innovation and predictive parts, the term whose energy is to be minimized is then: x not + x PRED not - x ⁇ B + k not + x ⁇ PRED B + k not
  • b w , PRED B + k not x PRED not - x ⁇ PRED B + k not .
  • This prediction b w , PRED B + k not is added to the input signal x ( n ) at 405 to obtain the modified input signal x ' ( n ) of the quantizer of the improvement stage k.
  • the quantization of x ' ( n ) is carried out at 306 by the quantization module of the improvement stage k, to give the quantization index I enh B + k not the improvement stage k and the decoded signal x B + k ( n ) of the stage k.
  • the module 307 gives the index of the code word I enh B + k not (1 bit in the illustrative example) of the adaptive dictionary D k which minimizes the quadratic error between x ' ( n ) and the quantization values d 1 B + k not and d 2 B + k not .
  • This index is to be concatenated with the index of the nested encoder preceding I B + k- 1 in order to obtain at the decoder the index of the codeword of the stage k I B + k .
  • the preprocessing operations of the block 310 thus make it possible to format the improvement coding noise of the stage k by performing a perceptual weighting of the input signal x (n). It is the input signal itself that is perceptually weighted and not an error signal as is the case in state-of-the-art methods.
  • a step of adding the prediction signal b w , pred B + k not to the signal x ( n ) is performed to give the modified signal x '( n ).
  • the quantification step of the modified signal x ' ( n ) is performed by the quantization module 306, in the same way as that explained with reference to the figures 3 and 4 .
  • the quantization of block 306 outputs the index I enh B + k not and the decoded signal at the stage k x B + k ( n ) .
  • a step of subtracting the reconstructed signal x B + k (n) from the signal x ( n ) is performed to give the reconstructed noise b B + k ( n ) .
  • a step of adding the prediction signal b w , pred B + k not at the signal b B + k ( n ) is performed to give the filtered reconstructed noise b w B + k not .
  • the figure 6 illustrates yet another embodiment of the pretreatment block 310 where here the difference lies in the way the filtered reconstructed signal b w B + k not is calculated.
  • Reconstructed noise filtered b w B + k not is obtained here by subtracting the reconstructed signal x B + k ( n ) from the signal x ' ( n ) at 614.
  • Module 707 gives the index of the code word I enh B + k not (1 bit in the illustrative example) of the adaptive dictionary D k ' which minimizes the squared error between x " ( n ) and the code words d 1 B + k ' not and d 2 B + k ' not .
  • This index is to be concatenated with the index of the preceding nested encoding I B + k -1 to obtain at the decoder the index of the current nested encoding I B + k comprising the stage k.
  • a step of updating the memories of the filter W ( z ) is performed at 311, to obtain memories that correspond to an input x ( n ) -x B + k ( n ) .
  • the solution on the figure 7 is equivalent in terms of quality and storage to that of the figure 3 but requires less computation in the case where the enhancement stage uses more than one bit. Instead of adding the predicted value x P B not to all code words (> 2) we only subtract before quantization and add to find the quantized value x B + k ( n ) . The complexity is reduced.
  • it is the prediction signal x P B not which is quantified by minimizing the quadratic error.
  • the figure 8 details a possible realization of a noise shaping at the heart coding.
  • the surrounding portion 807 can be seen and implemented as a noise shaping pretreatment that modifies the input of the standard encoder / decoder chain.
  • an encoder 900 as described according to the various embodiments above typically comprises a ⁇ P processor cooperating with a memory block BM including a storage and / or working memory, as well as 'a memory MEM buffer mentioned above as a means for storing, for example, a dictionary of quantization reconstruction levels or any other data necessary for the implementation of the coding method as described with reference to FIGS. figures 3 , 4 , 5 , 6 and 7 .
  • This encoder receives as input successive frames of the digital signal x (n) and delivers concatenated quantization indices I B + k .
  • the memory block BM may comprise a computer program comprising the code instructions for implementing the steps of the method according to the invention when these instructions are executed by a ⁇ P processor of the encoder and in particular the steps of obtaining possible values of quantification.
  • for the current improvement stage k by determining absolute reconstruction levels of the single current stage k from the indices of the preceding nested encoder, quantization of the input signal of the hierarchical coder which has or has not undergone perceptual weighting processing (x (n) or x '(n)), from said possible quantization values to form a quantization index of the stage k and a quantized signal corresponding to one of the possible quantization values.
  • a storage means readable by a computer or a processor, whether or not integrated into the encoder, possibly removable, stores a computer program implementing a coding method according to the invention.
  • the Figures 3 to 7 can for example illustrate the algorithm of such a computer program.

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Claims (8)

  1. Verfahren zur Codierung eines digitalen Eingangsaudiosignals (x(n)) in einem hierarchischen Codierer, umfassend eine Kerncodierstufe mit B Bits und mindestens eine laufende Verbesserungscodierstufe k, wobei die Kerncodierung und die Codierung der Verbesserungsstufen vor der laufenden Stufe k Quantifizierungsindizes liefern, die konkateniert sind, um die Indizes des vorhergehenden verschachtelten Codierers (IB+k-1) zu bilden, wobei das Verfahren dadurch gekennzeichnet ist, dass es die folgenden Schritte umfasst:
    - Erhalt (303) von möglichen Quantifizierungswerten (di B+k(n)) für die laufende Verbesserungsstufe k aus absoluten Rekonstruktionsniveaus (yi b+k) der einzigen laufenden Stufe k und den Indizes des vorhergehenden verschachtelten Codierers (iB+k-1);
    - Quantifizierung (306) des Eingangssignals des hierarchischen Codierers, das einer perzeptuellen Gewichtungsbehandlung (x(n) oder x'(n)) aus den möglichen Quantifizierungswerten (di B+k(n)) unterzogen wurde oder nicht, um einen Quantifizierungsindex der Stufe k (Ienh B+k(n)) und ein quantifiziertes Signal (x̃ B+k (n)) entsprechend einem der möglichen Quantifizierungswerte zu bilden.
  2. Verfahren nach Anspruch 1, dadurch gekennzeichnet, dass das Eingangssignal einer perzeptuellen Gewichtungsbehandlung unterzogen wurde, die einen vorbestimmten Gewichtungsfilter verwendet, um ein modifiziertes Eingangssignal x'(n) vor dem Quantifizierungsschritt (306) zu ergeben, und dass es ferner einen Anpassungsschritt (311) der Speicher des Gewichtungsfilters aus dem quantifizierten Signal (x̃ B+k (n)) der laufenden Verbesserungscodierungsstufe umfasst.
  3. Verfahren nach Anspruch 1, dadurch gekennzeichnet, dass die möglichen Quantifizierungswerte für die Verbesserungsstufe k ferner einen Skalenfaktor und einen Vorhersagewert, der von der Kerncodierung adaptativen Typs stammt, enthalten.
  4. Verfahren nach Anspruch 2, dadurch gekennzeichnet, dass das zu quantifizierende modifizierte Eingangssignal (x"(n)) in der Verbesserungsstufe k das perzeptuell gewichtete Eingangssignal ist, dem ein Vorhersagewert, der von der Kerncodierung adaptativen Typs stammt, entnommen wird.
  5. Verfahren nach Anspruch 1 bis 4, dadurch gekennzeichnet, dass die perzeptuelle Gewichtungsbehandlung durch Vorhersagefilter erfolgt, die einen Filter des Typs ARMA bilden.
  6. Hierarchischer Codierer eines digitalen Eingangsaudiosignals (x(n)), umfassend eine Kerncodierstufe mit B Bits und mindestens eine laufende Verbesserungscodierstufe k, wobei die Kerncodierung und die Codierung der Verbesserungsstufen vor der laufenden Stufe k Quantifizierungsindizes liefern, die konkateniert sind, um die Indizes des vorhergehenden verschachtelten Codierers (IB+k-1) zu bilden, wobei der Codierer dadurch gekennzeichnet ist, dass er umfasst:
    - ein Modul für den Erhalt (303) von möglichen Quantifizierungswerten (di B+k(n)) für die laufende Verbesserungsstufe k durch die Bestimmung von absoluten Rekonstruktionsniveaus der einzigen laufenden Stufe k aus den Indizes des vorhergehenden verschachtelten Codierers (iB+k-1);
    - ein Modul zur Quantifizierung (306) des Eingangssignals des hierarchischen Codierers, das einer perzeptuellen Gewichtungsbehandlung (x(n) oder x'(n)) aus den möglichen Quantifizierungswerten (di B+k(n)) unterzogen wurde oder nicht, um einen Quantifizierungsindex der Stufe k (Ienh B+k(n)) und ein quantifiziertes Signal (x̃ B+k (n)) entsprechend einem der möglichen Quantifizierungswerte zu bilden.
  7. Hierarchischer Codierer nach Anspruch 6, dadurch gekennzeichnet, dass er ferner ein Vorbehandlungsmodul (310) zur perzeptuellen Gewichtung, das einen vorbestimmten Gewichtungsfilter verwendet, um ein modifiziertes Eingangssignal (x'(n)) am Eingang des Quantifizierungsmoduls (306) zu ergeben, und ein Anpassungsmodul (311) der Speicher des Gewichtungsfilters aus dem quantifizierten Signal (x̃ B+k (n)) der laufenden Verbesserungscodierungsstufe umfasst.
  8. Informatikprogramm, umfassend Codebefehle für den Einsatz der Schritte des Codierungsverfahrens nach einem der Ansprüche 1 bis 5, wenn diese Befehle von einem Prozessor ausgeführt werden.
EP11811097.2A 2010-12-16 2011-12-13 Verbesserte kodierung einer verbesserungsstufe bei einem hierarchischen kodierer Not-in-force EP2652735B1 (de)

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FR1060631A FR2969360A1 (fr) 2010-12-16 2010-12-16 Codage perfectionne d'un etage d'amelioration dans un codeur hierarchique
PCT/FR2011/052959 WO2012080649A1 (fr) 2010-12-16 2011-12-13 Codage perfectionne d'un etage d'amelioration dans un codeur hierarchique

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FR2938688A1 (fr) * 2008-11-18 2010-05-21 France Telecom Codage avec mise en forme du bruit dans un codeur hierarchique
EP2980793A1 (de) * 2014-07-28 2016-02-03 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Codierer, Decodierer, System und Verfahren zur Codierung und Decodierung
CN105679312B (zh) * 2016-03-04 2019-09-10 重庆邮电大学 一种噪声环境下声纹识别的语音特征处理方法
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EP1483759B1 (de) * 2002-03-12 2006-09-06 Nokia Corporation Skalierbare audiokodierung
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WO2012080649A1 (fr) 2012-06-21
CN103370740A (zh) 2013-10-23
KR20140005201A (ko) 2014-01-14
JP5923517B2 (ja) 2016-05-24
CN103370740B (zh) 2015-09-30

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