EP2153438A1 - Post-traitement de reduction du bruit de quantification d'un codeur, au decodage - Google Patents
Post-traitement de reduction du bruit de quantification d'un codeur, au decodageInfo
- Publication number
- EP2153438A1 EP2153438A1 EP08805992A EP08805992A EP2153438A1 EP 2153438 A1 EP2153438 A1 EP 2153438A1 EP 08805992 A EP08805992 A EP 08805992A EP 08805992 A EP08805992 A EP 08805992A EP 2153438 A1 EP2153438 A1 EP 2153438A1
- Authority
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- European Patent Office
- Prior art keywords
- signal
- quantization noise
- coding
- information
- compression
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000011002 quantification Methods 0.000 title abstract description 8
- 238000012805 post-processing Methods 0.000 title description 3
- 238000007906 compression Methods 0.000 claims abstract description 39
- 238000012545 processing Methods 0.000 claims abstract description 39
- 230000006835 compression Effects 0.000 claims abstract description 38
- 238000013139 quantization Methods 0.000 claims description 123
- 230000006870 function Effects 0.000 claims description 30
- 238000000034 method Methods 0.000 claims description 20
- 238000001914 filtration Methods 0.000 claims description 14
- 230000003595 spectral effect Effects 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 3
- 230000015654 memory Effects 0.000 claims description 2
- 238000004040 coloring Methods 0.000 claims 1
- 238000011282 treatment Methods 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 5
- 238000001228 spectrum Methods 0.000 description 4
- 230000003044 adaptive effect Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 241001123248 Arma Species 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000003071 parasitic effect Effects 0.000 description 1
- 238000011946 reduction process Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000005236 sound signal Effects 0.000 description 1
- 238000010186 staining Methods 0.000 description 1
- 230000003936 working memory Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
- G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
- G10L19/26—Pre-filtering or post-filtering
-
- 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
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0316—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude
- G10L21/0364—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude for improving intelligibility
-
- 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
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
Definitions
- the present invention relates to signal processing, in particular digital signals in the telecommunications field, these signals being, for example, speech, music, video signals, or other signals.
- the rate needed to pass an audio and / or video signal with sufficient quality is an important parameter in telecommunications.
- audio coders have been developed in particular to compress the amount of information necessary to transmit a signal.
- Some encoders achieve particularly high information compression rates. Such coders generally use advanced information modeling and quantification techniques. Thus, such encoders transmit only models or partial data of the signal.
- the decoded signal although not identical to the original signal (since some of the information has not been transmitted due to the quantization operation), nevertheless remains very close to the original signal.
- Quantization noise The difference, from a mathematical point of view, between the decoded signal and the original signal is then called "quantization noise”.
- disortion introduced by the coding / decoding.
- a perceptual post-filter of the type used, for example, in CELP type speech decoders (for "Code Excited Linear Prediction"). This is to perform a filtering that improves the subjective quality at the price of a distortion. Indeed, a signal attenuation is applied in the areas where the quantization noise is most audible (especially between the formants).
- Current perceptual postfilters provide good results for speech signals, but poorer results for other types of signals (music signals, for example).
- Adaptive Postfiltering for Quality Enhancement of Coded Speech Chen J.H., Gersho A., IEEE Trans. On Speech and Audio Pr oc, (January 1995).
- the described model is based on a division into two sections: - a "long-term” section reinforces the harmonics (harmonics of the fundamental frequency) and widens the spectral valleys between these harmonics, and a “short-term” section strengthens the formants and also hollow out the spectral valleys between these formants.
- Harmonics and formants are well-known spectral characteristics of speech but applying this type of processing to a signal other than speech generates strong distortions. For example, the spectral richness of a music signal can not be handled with such a simple signal model.
- perceptual post-filters can generate distortions, because they rely on a model that is not precise enough. Moreover, the perceptual post filter is usually ineffective in periods of silence.
- Another treatment family targets conventional noise reduction treatments to distinguish the useful signal from the noise.
- This type of processing therefore makes it possible to reduce the noise related to the environment of the signal capture and it is often used for speech signals.
- coding / decoding one may want to transmit the ambient noise and it is then desirable that the noise reduction does not apply to this type of noise.
- the present invention improves the situation.
- the method according to the invention comprises an estimation of a quantization noise introduced by the compression coding from information obtained a priori on the type of compression coding, said information being independent of the characteristics of the signal and a determination from the estimated quantization noise, a filtering function to be applied to the decoded signal for applying an estimated quantization noise reduction processing.
- noise reduction processing is understood to mean an operation of the type described above which consists in extracting the useful signal from a signal to be processed, by filtering the parasitic signals, for example by defining a gain function intervening in a filter applied to the decoded signal.
- the quantization noise is filtered.
- a quantization noise reduction model is chosen from information on the type of compression coding, and a quantization noise reduction processing is applied to the decoded signal according to the chosen model.
- the quantization noise introduced by the compression coding is estimated, and a filtering function (and notably the parameters of this function) is determined from the estimated quantization noise; filtering) to be applied to the decoded signal to apply the quantization noise reduction processing.
- noise reduction processing specific to each type of compression coding performed.
- the very way of estimating the characteristics of the noise reduction filter depends on the type of coding performed.
- the quantization noise itself strongly depends on the type of coding performed. It will be seen that it is possible to establish a variation of the quantization noise as a function of a variation of the decoded signal, and that this variation of the quantization noise is specific to the type of coding implemented.
- the quantization noise is estimated to determine the filtering function to be applied to the decoded signal having this current parameter value.
- the information on the type of compression coding is a priori information, independent of the characteristics of the signal, and that, advantageously, it can be deduced from it: a variation model of a signal-to-quantization noise ratio, in a function of at least one parameter of the decoded signal, and / or a spectral staining of the quantization noise (i.e., a spectral variation of the quantization noise as a function of the characteristics of the decoded signal).
- the prior information on the type of compression coding is obtained during an encoder declaration procedure.
- the invention is particularly suitable in the case where the type of compression coding is a coding according to the G.711 standard.
- the present invention also provides a device for processing a signal initially coded in compression according to a predetermined type of coding, and then decoded.
- the device comprises: means for estimating a quantization noise introduced by the compression coding, on the basis of information obtained a priori on the type of compression coding, said information being independent of the characteristics of the signal, and means for determining, from the estimated quantization noise, a filtering function to be applied to the decoded signal to apply an estimated quantization noise reduction processing.
- the device advantageously comprises means for implementing the method described above.
- the present invention also relates to a computer program, intended to be stored in memory of a processing device of the aforementioned type, and comprising instructions for calculating the quantization noise, as well as parameters of a quantization noise reduction filter. when these instructions are executed by a processor of the processing device.
- An advantageous embodiment may consist in providing a set of instructions for each type of coding implemented and, in each set of instructions, defining a variation of the quantization noise as a function of the decoded signal.
- a set of appropriate instructions is selected upon receipt of the information a priori.
- the quantization noise present in the decoded signal is calculated, and the parameters of the post-filter are calculated in correspondence of this quantization noise, to limit or even eliminate this noise.
- the instructions on the variation of the quantization noise can be programmed offline, on the basis of observations (theoretical or experimental according to the exemplary embodiments which will be described later) made on the type of coding used.
- the invention proposes a post-processing performed after decoding and which uses a priori information on the characteristics of the quantization operation performed by the encoder.
- the type of processing (or "processing model" according to the generic terms above) that will be chosen to process the signal is independent of the characteristics of the signal itself.
- the processing itself in particular the estimation of the gain function
- the type of treatment is the same and is not based, for example, on only on the energy of a decoded frame received.
- the invention makes it possible to reduce the quantization noise (and therefore the distortion) that a compression coder of the signal implementing a quantization operation usually introduces.
- the invention advantageously reduces the quantization noise alone, even during periods of silence, and this, for any type of signal.
- the implementation of the invention does not perform a conventional noise reduction and therefore does not modify the noise related to the environment of the capture of the signal.
- the implementation of the invention makes it possible to reduce or even eliminate the quantization noise, without distorting the signal, and this, for any type of signal, simply by using prior information on the type of encoder used (for example the characteristics of the encoder compression model, the characteristics of the quantizer, or other).
- the present invention finds an advantageous application to the field of speech and music processing, and more generally to the processing of the signal, especially images, since any encoder is required to introduce a quantization noise.
- the invention applies to all areas where it is sought to reduce a quantization noise of a signal.
- FIG. 1 schematically illustrates the general structure of a processing unit within the meaning of the invention
- FIG. 2 diagrammatically illustrates the steps of a method in the sense of the invention
- FIG. 3 illustrates a variation of the compression law (called "A-law") of the amplitudes, in a coding according to the G.711 standard for illustrate an embodiment of the invention
- FIG. 4 illustrates the variation of the signal-to-quantization noise ratio RSB as a function of the load factor, this variation being drawn from the variation illustrated in FIG. 3, FIG.
- FIG. 5 illustrates the steps of an example of processing in the case of a coding according to the G.711 standard, based notably on the observations of the variations of FIGS. 3 and 4,
- FIG. 6 illustrates an example of the spectrum of the signal. (dashed curve) and the quantization noise spectrum (continuous curve) for standard coding
- FIG. 7 illustrates an example of a waveform of a speech signal S * (top curve) and the corresponding quantization signal-to-noise ratio RSB (bottom curve), for a coding / decoding according to the norm G .722,
- - Figure 8 is a scatterplot illustrating for each segment of 80 samples the correlation between the SNR signal-to-noise ratio and the signal energy, in an application to G.722 coding / decoding
- Fig. 9 shows the signal segments (in black) where the error of the RSB quantization signal to noise ratio estimate is greater than 6 dB while the RSB ratio is less than 25 dB, in the application to a coding / decoding according to G.722,
- FIG. 10 shows the point cloud representing, for each segment, the energy of the noise as a function of the energy of the signal, illustrating here the estimation of the noise level (line in phantom), the zone or the estimate error is less than 6 dB (dashed lines), and the delineation for which the RSB is greater than 25 dB (solid line).
- a signal S is: coded in compression by a coder COD of known type and applying in particular a quantization operation Q to the signal S, transmitted via a transmission channel CA, then
- the signal thus decoded, denoted S then has a quantization noise which is defined mathematically as a difference (S - S) with respect to the original signal S.
- a quantization noise reduction processing unit TBQ is provided, in the sense of the invention, downstream of the decoder DEC to suppress or at least limit the quantization noise in the signal S .
- the unit TBQ comprises at least one input E to receive decoder DEC INF information on the type of coding / decoding implemented, which then allows to choose a noise reduction treatment model to be implemented. artwork.
- it is estimated from the signal received and decoded S * , and depending on the type of coding / decoding that has been implemented, the influence of the quantization noise in the received signal S.
- a calculation module is provided to give an estimate of the quantization noise BQ, on the basis of the model chosen and as a function of the received signal S.
- This calculation module can typically be in the form of a combination of a processor and a working memory (not shown).
- the estimated noise BQ is simply processed by applying a conventional filtering FIL to the signal S to finally output a processed signal S T.
- a conventional filtering FIL for example a gain function for the signal filtering
- step S3 from the information INF received on the type of coding / decoding used (step S2), a model (step S3) of noise reduction processing is determined.
- the quantization noise reduction model chosen may be different, for example depending on whether the signal has been coded / decoded according to the G.711 standard or coded / decoded according to the standard. G.722.
- step S4 when the signal is received in successive blocks (or frames marked TRi in step S1), it is estimated (step S4) a quantization noise level specific to the chosen model.
- a quantization noise level specific to the chosen model.
- This RSB information depends on the decoded signal S * , but also on the type of coding implemented.
- prior knowledge of the coding by obtaining the information INF allows, together with certain statistical characteristics of the signal S, to estimate here the signal-to-noise ratio RSB.
- This step S4 therefore requires knowing a priori the type of encoder that has been used, information that can be obtained for example during a declaration procedure of the encoder called "encoder transaction", which is supposed to be acquired.
- the type of encoder, the characteristics of its compression model and its quantizer Q make it possible to estimate an evolution of the signal-to-quantization noise ratio, as a function of certain statistical parameters of the signal, for example its variance, its spectral density of power, or others.
- This relationship between the signal-to-quantization noise ratio and the statistical parameters of the signal involves coder-specific laws which will be described later, for some exemplary embodiments.
- the necessary statistical parameters can be calculated by classical quantity estimators (eg variance). Based on these estimates, an estimate of the signal to noise quantization ratio can be extrapolated.
- the estimates can be made indifferently in the time domain, frequency, or any other time-frequency domain (wavelet for example).
- the next step S5 consists in calculating the parameters of the filter for the reduction of the quantization noise in the received signal S.
- Knowledge of the signal-to-noise ratio makes it possible to deduce the expression of a quantization noise reduction filter, this filter being hereinafter called "post-filter” (downstream of the decoder). It is indeed possible to deduce the expression of a digital filter whose purpose is to reduce a noise whose most characteristics are known a priori (its power spectral density for example) and whose level is determined from the estimation of the quantization signal-to-noise ratio obtained at the step previous S4.
- the calculation of the filter can be performed in the frequency domain and implement any short-term spectral attenuation technique (a spectral subtraction, a Wiener filter, or other).
- the calculation of the post-filter in step S5 can be performed in the time domain, frequency domain, or any other time-frequency domain.
- step S6 itself, here amounts to filtering the decoded signal S * by the post-filter calculated in step S5.
- This step S6 can be performed in the time or frequency domain, according to the constraints related to the implementation and the estimation domain of the PAR parameters and the RSB ratio in the previous steps.
- a TRi 'frame processed by denoising the quantization noise in step S7 is obtained.
- the quantization signal-to-noise ratio depends on the variance ⁇ x 2 of the signal, the saturation levels Jc 1113x determined by the dynamics and of course the number of bits b used for the representation of the samples, according to an expression of the type:
- the expression (1) is strongly dependent on the value of this parameter F.
- the maximum signal-to-noise ratio is obtained for a full-scale signal and that it decreases rapidly if the amplitude of the signal decreases.
- RSB umf (20 log 2> + 10 log 3 + 10 log [A / (l + In A)] - 20 log (r) [dB] RSB umf "6.02b + 4.77 + 101og [A / (l + InA )] -201og (r) [dB] (3)
- a first increasing part corresponding to the uniform variation of the law of compression
- - a following part constant, corresponding to the logarithmic variation of this law.
- the implementation of the quantization noise reduction processing is based on the exploitation of this information a priori. It requires in particular to carry out a estimation of the load factor F, the parameter on which the power of the quantization noise depends, as follows.
- the average power Pm of a current block TRi (step S52) is estimated, and hence the load factor F, varying as the inverse of the square root of the average power (step S53). It is considered that the numerator -Xmax of the load factor is here constant (at constant saturation level).
- the found value of the load factor F is compared with that of a threshold F 8 defining the point of inflection of the compression law (FIG.
- the signal-to-quantization noise ratio RSB can be calculated according to a linear variation as a function of the load factor derived from equation (3):
- the gain function (step S57) is then evaluated for post-filter application (step S58).
- a Wiener filter can be provided as gain function g (RSB).
- noise reduction processing in particular for signals of low signal-to-quantization noise ratio, ie at low amplitude (for load factors such as -20.1og (F) ⁇ - 5OdB in FIG. 4), possibly providing for: - post-filter thresholding, and / or voice activity detector for speech signals (with lighter quantization noise reduction processing during periods of speech inactivity).
- a variant of the treatment presented here is to reduce the quantization noise, sample by sample, rather than a treatment by successive blocks.
- the load factor is directly given by the amplitude level of the sample (inverse of the square root of the amplitude) and the continuation of the treatment is similar to that presented above.
- the ITU-T G.722 coding standardized in 1988 for 64 kbit / s digital audio conferencing applications, is still very widely used. It is a hierarchical coding / decoding at three rates: 64, 56 and 48 kbit / s.
- the signal is divided into two subbands by a filter called QMF (for "Quadrature Mirror Filter”).
- QMF for "Quadrature Mirror Filter”
- the two bands obtained are encoded with an ADPCM encoder (for "Modulation by
- ADPCM Adaptive Differential Pulse Code Modulation
- the high band is coded on 2 bits per sample.
- the difference between the three rates comes from the low band which is coded on 6 bits per sample for the highest rate, but it is possible to reserve the last or last two bits for data transmission.
- the quality of the higher bit rate is very good, but the coding noise becomes very audible and annoying for the lowest bit rate at 48 kbit / s.
- the reduction treatment of the Quantization noise in the sense of the invention can be advantageously applied in this case.
- the characteristics of the quantization noise can be efficiently estimated from the decoded signal.
- the spectrum of quantization noise (full line curve) is always flat, regardless of the signal spectrum (dotted line curve).
- the signal to quantization noise ratio depends on the average signal strength and its nature.
- FIG. 7 it can be observed that the signal-to-quantization noise ratio (RSB) correlates well with the average power of the signal S.
- the RSB ratio was estimated on segments of 80 samples (5 ms for a sampling frequency of 16 kHz).
- the cloud-like representation of FIG. 8 further illustrates the correlation between the mean signal power (abscissa) and the quantization noise-to-signal ratio (y-axis), calculated by segments of 80 samples.
- FIG. 9 represents in black on a gray background the areas of the signal where the estimation error of the ratio RSB is greater than 6 dB, and the ratio RSB itself is less than 25 dB, i.e. the signal areas in which the estimator underestimates the quantization noise, resulting in a lower efficiency of the quantization noise reduction processing. It can nevertheless be noted that these zones correspond to unvoiced signal segments, for which the quantization noise is less troublesome because of the intrinsically noisy nature of the signal.
- FIG. 10 shows a power diagram of the noise with respect to a signal power, in accordance with the empirical equation (5).
- the dashed line represents the estimate of the noise power.
- the dashed lines delimit the area where the error of the estimate is less than 6 dB. Below the solid line, the RSB is greater than 25 dB.
- the black dots correspond to the black segments of Figure 9.
- the estimation of the RSB ratio can be further refined by taking into account, for example, the prediction gain of the ARMA (autoregressive) filters which intervene in the G.722 decoder. Knowing the spectral shape of the quantization noise and its energy, the quantization noise reduction process of the invention can be effectively applied for this type of coding / decoding. This example is obviously valid for other types of coding / decoding of the same family as those standardized G.726 or G.727.
- an advantageous application of the invention can for example aim to reduce the quantization noise of a standardized ITU-G.711 encoder by using the properties of the quantization law implemented. , especially according to law A in Europe. Indeed, in this application, the quantization noise is white and it is possible to estimate the quantization signal-to-noise ratio and hence a gain function that makes it possible to reduce this noise.
- An advantageous application of the invention therefore aims to reduce quantization noise in the processing at the extended band extension of the G.711 encoder (ITU-T SG16, G.71 IWB).
- the invention applies to any type of coding / decoding as long as its intrinsic characteristics are known.
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- Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
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Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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FR0704242 | 2007-06-14 | ||
PCT/FR2008/051057 WO2009004225A1 (fr) | 2007-06-14 | 2008-06-13 | Post-traitement de reduction du bruit de quantification d'un codeur, au decodage |
Publications (2)
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EP2153438A1 true EP2153438A1 (fr) | 2010-02-17 |
EP2153438B1 EP2153438B1 (fr) | 2011-10-26 |
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EP08805992A Active EP2153438B1 (fr) | 2007-06-14 | 2008-06-13 | Post-traitement de reduction du bruit de quantification d'un codeur, au decodage |
Country Status (6)
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US (1) | US8175145B2 (fr) |
EP (1) | EP2153438B1 (fr) |
JP (2) | JP2010529511A (fr) |
AT (1) | ATE531038T1 (fr) |
ES (1) | ES2376178T3 (fr) |
WO (1) | WO2009004225A1 (fr) |
Families Citing this family (10)
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RU2470385C2 (ru) * | 2008-03-05 | 2012-12-20 | Войсэйдж Корпорейшн | Система и способ улучшения декодированного тонального звукового сигнала |
JP5141633B2 (ja) * | 2009-04-24 | 2013-02-13 | ソニー株式会社 | 画像処理方法及びそれを用いた画像情報符号化装置 |
US8886523B2 (en) * | 2010-04-14 | 2014-11-11 | Huawei Technologies Co., Ltd. | Audio decoding based on audio class with control code for post-processing modes |
JP5898515B2 (ja) * | 2012-02-15 | 2016-04-06 | ルネサスエレクトロニクス株式会社 | 半導体装置及び音声通信装置 |
TR201910989T4 (tr) * | 2013-03-04 | 2019-08-21 | Voiceage Evs Llc | Bir zaman-bölgesi kod çözücüsünde nicemleme gürültüsünün azaltılmasına yönelik cihaz ve yöntem. |
FR3007184A1 (fr) * | 2013-06-14 | 2014-12-19 | France Telecom | Controle du traitement d'attenuation d'un bruit de quantification introduit par un codage en compresssion |
JP5816992B2 (ja) * | 2013-10-31 | 2015-11-18 | 株式会社アクセル | フィルタの設計方法及びそのフィルタを備えた音響再生装置 |
EP2887350B1 (fr) * | 2013-12-19 | 2016-10-05 | Dolby Laboratories Licensing Corporation | Filtrage adaptatif du bruit de quantification de données audio décodé |
US9881630B2 (en) * | 2015-12-30 | 2018-01-30 | Google Llc | Acoustic keystroke transient canceler for speech communication terminals using a semi-blind adaptive filter model |
JP2016105188A (ja) * | 2016-01-12 | 2016-06-09 | 株式会社アクセル | 音声信号圧縮装置及び音声信号圧縮方法 |
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JPH03116197A (ja) * | 1989-09-29 | 1991-05-17 | Matsushita Electric Ind Co Ltd | 音声復号化装置 |
JP3024468B2 (ja) * | 1993-12-10 | 2000-03-21 | 日本電気株式会社 | 音声復号装置 |
JP4358221B2 (ja) * | 1997-12-08 | 2009-11-04 | 三菱電機株式会社 | 音信号加工方法及び音信号加工装置 |
US6128346A (en) * | 1998-04-14 | 2000-10-03 | Motorola, Inc. | Method and apparatus for quantizing a signal in a digital system |
US6115689A (en) * | 1998-05-27 | 2000-09-05 | Microsoft Corporation | Scalable audio coder and decoder |
JP2000269821A (ja) * | 1999-03-18 | 2000-09-29 | Oki Micro Design Co Ltd | 予測符号化信号復号化装置及び雑音除去方法 |
US7453942B2 (en) * | 2002-01-25 | 2008-11-18 | Nxp B.V. | Method and unit for substracting quantization noise from a PCM signal |
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KR100477699B1 (ko) * | 2003-01-15 | 2005-03-18 | 삼성전자주식회사 | 양자화 잡음 분포 조절 방법 및 장치 |
AU2003274864A1 (en) * | 2003-10-24 | 2005-05-11 | Nokia Corpration | Noise-dependent postfiltering |
WO2005099243A1 (fr) * | 2004-04-09 | 2005-10-20 | Nec Corporation | Méthode et dispositif de communication audio |
CN101199005B (zh) * | 2005-06-17 | 2011-11-09 | 松下电器产业株式会社 | 后置滤波器、解码装置以及后置滤波处理方法 |
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- 2008-06-13 WO PCT/FR2008/051057 patent/WO2009004225A1/fr active Application Filing
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ES2376178T3 (es) | 2012-03-09 |
EP2153438B1 (fr) | 2011-10-26 |
JP2010529511A (ja) | 2010-08-26 |
US20100183067A1 (en) | 2010-07-22 |
JP5881791B2 (ja) | 2016-03-09 |
ATE531038T1 (de) | 2011-11-15 |
JP2015007805A (ja) | 2015-01-15 |
WO2009004225A1 (fr) | 2009-01-08 |
US8175145B2 (en) | 2012-05-08 |
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