EP1386307B2 - Procede et dispositif pour determiner un niveau de qualite d'un signal audio - Google Patents

Procede et dispositif pour determiner un niveau de qualite d'un signal audio Download PDF

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Publication number
EP1386307B2
EP1386307B2 EP02703438.8A EP02703438A EP1386307B2 EP 1386307 B2 EP1386307 B2 EP 1386307B2 EP 02703438 A EP02703438 A EP 02703438A EP 1386307 B2 EP1386307 B2 EP 1386307B2
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Prior art keywords
signal
audio signal
quality
determining
interruptions
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EP1386307A1 (fr
EP1386307B1 (fr
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Pero Juric
Bendicht Thomet
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Swissqual License AG
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Swissqual License AG
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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
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/69—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for evaluating synthetic or decoded voice signals

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  • the invention relates to a method for determining a quality measure of an audio signal. Furthermore, the invention relates to an apparatus for carrying out this method as well as a noise suppression module and an interruption detection and interpolation module for use in such a device.
  • the assessment of the quality of a telecommunications network is an important tool for achieving or maintaining a desired service quality.
  • One way to assess the quality of service of a telecommunications network is to determine the quality of a signal transmitted over the telecommunications network.
  • various intrusive methods are known for this purpose.
  • the system to be tested intervenes by occupying a transmission channel and transmitting a reference signal therein.
  • the quality assessment is then carried out by comparing the known reference signal with the received signal, for example subjectively by one or a plurality of test persons.
  • this is expensive and therefore expensive.
  • intrusive methods generally have the disadvantage that, as already mentioned, the system to be tested must be intervened. In order to determine the signal quality, at least one transmission channel must be occupied and a reference signal transmitted therein. This transmission channel can not be used for data transmission during this time. In addition, while it is in principle possible for a broadcasting system such as a broadcasting service to occupy the signal source for the transmission of test signals, as this would occupy all channels and the test signal would be transmitted to all receivers, this approach is extremely impractical. Intrusive techniques are also unsuitable for simultaneously monitoring the quality of a variety of transmission channels.
  • EP-A-644 526 discloses a non-intrusive noise reduction method which uses an estimate of noise energy to calculate the desired signal information.
  • US-A-5,848,384 shows a method and apparatus for determining the quality of an audio signal.
  • the object of the invention is to specify a method of the abovementioned type, which avoids the disadvantages of the prior art and, in particular, offers a possibility for assessing the signal quality of a signal transmitted via a telecommunication network without knowledge of the originally transmitted signal.
  • a reference signal is first determined from the audio signal.
  • a quality value is determined which is used to determine the quality measure.
  • the inventive method thus allows an assessment of the quality of an audio signal at any terminal of the telecommunications network. Ie. it also allows the quality assessment of many transmission channels simultaneously, even allowing simultaneous evaluation of all channels.
  • the quality assessment is carried out solely on the basis of the properties of the received signal, d. H. without knowledge of the source signal or the signal source.
  • the invention thus enables not only a monitoring of the transmission quality of the telecommunications network, but also, for example, a quality-based cost accounting, quality-based routing in the network, a test of the degree of coverage, for example in mobile networks, a QOS (Quality of Service) control of network nodes or a quality comparison within a network or across networks.
  • a quality-based cost accounting for example, a quality-based cost accounting
  • quality-based routing in the network for example in mobile networks
  • QOS Quality of Service
  • An audio signal transmitted via a telecommunication network typically has, in addition to the desired signal information, also undesired components, such as various noise components, which were not present in the original source signal.
  • the best possible estimate of the originally transmitted signal is necessary.
  • To reconstruct this reference signal there are several methods. One possibility is to determine an estimate of the characteristics of the transmission channel and quasi backwards from the received signal. Another possibility is a direct estimation of the reference signal based on the known information about the received signal and the transmission channel.
  • the reference signal is determined by estimating the noise components present in the received signal and then removing them from the received signal. By removing the noise components from the audio signal, a denoised audio signal is first determined, which is preferably used as a reference signal for assessing the transmission quality.
  • the audio signal could, for example, be passed through appropriate filters.
  • a preferred method of removing the noise from the audio signal uses a neural network.
  • the audio signal is not used directly as an input signal.
  • a discrete wavelet transform DWT
  • This transformation provides a plurality of DWT coefficients of the audio signal which are fed to the neural network as an input.
  • the neural network provides at the output a plurality of corrected DWT coefficients, from which the reference signal is obtained with the inverse DWT. This corresponds to the noisy version of the audio signal.
  • the coefficients of the neural network must be set to provide the DWT coefficients of a noisy input signal the DWT coefficients of the corresponding noisy input signal.
  • the neural network For the neural network to provide the desired coefficients, it must first be trained with a set of corresponding noisy or noisy signal pairs.
  • any other information besides the quality value, which is determined by the comparison of the received audio signal with the reference signal determined therefrom, can also be taken into account. This may be information contained in the audio signal as well as information about the transmission channel or the telecommunication network itself.
  • the quality of the received audio signal is influenced, for example, by the codecs (coder-decoder) passed through during the transmission. It is difficult to detect such signal degradation because, for example, if the codec bit rates are too small, some of the original signal information is lost. However, too small codec bit rates result in a change in the fundamental frequency (pitch) of the audio signal, which is why it is advantageous to examine the course and dynamics of the fundamental frequency in the audio signal. Since such changes can be most easily examined on the basis of audio signal sections with vowels, signal components in the audio signal with vowels are preferably first detected and then examined for pitch variations.
  • the received audio signal may have more or less long signal interruptions.
  • the type of interpolation of the lost signal sections depends on the length of the signal interruption. For short breaks, d. H. with interruptions up to a few samples in the audio signal is preferably a polynomial and medium-length interruptions, d. H. from a few to a few dozen samples, model-based interpolation is preferred.
  • the received audio signal may include various types of audio signals. For example, it can contain voice, music, noise or silence signals.
  • the quality assessment may be based on all or part of these signal components. In a preferred variant of the invention, however, the assessment of the signal quality is limited to the speech signal components. With an audio discriminator, therefore, the audio signal components first become from the audio signal extracted and only these speech signal components for determining the quality measure, ie used to determine the reference signal. In this case, to determine the quality value, the determined reference signal is, of course, not compared with the received audio signal, but only with the speech signal component extracted therefrom.
  • the inventive device for machine-aided determination of a quality measure of an audio signal comprises first means for determining a reference signal from the audio signal, second means for determining a quality value by comparing the determined reference signal with the audio signal and third means for determining the quality measure taking into account the quality value.
  • the first means for determining a reference signal from the audio signal may comprise a plurality of modules.
  • a noise suppression module and / or an interruption detection and interpolation module is preferably provided.
  • the noise suppression module suppresses noise signal components in the received audio signal. It includes the means for performing the wavelet transforms described above as well as the neural network for determining the new DWT coefficients.
  • the interrupt detection and interpolation module has those means which are required on the one hand for detecting signal interruptions in the audio signal and on the other hand for polynomial interpolation of short as well as model-based interpolation of medium-length signal interruptions.
  • the thus determined reference signal thus corresponds to a noisy version of the received audio signal and typically has only greater signal interruptions.
  • the information about the signal discontinuities of the audio signal is not only used to obtain a better reference signal, it can also be used to determine a better quality measure.
  • the third means for determining the quality measure are therefore preferably designed such that information about signal interruptions in the audio signal can be taken into account.
  • the device therefore advantageously has fourth means for determining information about codec-related signal distortions.
  • fourth means for determining information about codec-related signal distortions include, for example, a vocal detection module with which signal components with vowels can be detected in the audio signal. These vocal signal components are passed on to an evaluation module, which uses these signal components to determine information about codec-related signal distortions, which are also used to assess the signal quality.
  • the third means are correspondingly designed such that this information about the codec-related signal distortions can be taken into account in the determination of the quality measure.
  • the device therefore has, in particular, fifth means for extracting the speech signal components from the audio signal. Accordingly, not the audio signal itself, but only its voice signal component is denoudized and examined for interruptions in order to determine the reference signal. Likewise, of course, not the audio signal, but only the voice signal component is compared with this reference signal. Thus, the determination of the quality measure takes place only on the basis of the information in the speech signal component, wherein the information from the remaining signal components is not taken into account.
  • FIG. 1 shows a block diagram of the inventive method.
  • a quality measure 2 is determined for an audio signal 1, which can be used, for example, for the evaluation of the used (not shown) telecommunications network.
  • the audio signal 1 is understood here to mean the signal which a receiver receives after the transmission via the telecommunication network.
  • This audio signal 1 is true typically not coincide with the signal transmitted by the transmitter (not shown) because on the way from the transmitter to the receiver, the transmission signal is varied in a variety of ways. For example, it goes through various modules such as speech coders and decoders, multiplexers and demultiplexers, as well as speech enhancers and echo cancellers. But even the transmission channel itself can have a large impact on the signal, which, for example, in the form of interference, fading, réellesab- or interruptions, echoes, etc. express.
  • the audio signal 1 thus contains not only desired signal components, d. H. the original transmission signal, but also unwanted interference signal components. It may also be that signal portions of the transmission signal are missing, d. H. lost during the transmission.
  • the assessment of the signal quality does not take place on the basis of the entire audio signal 1, but only on the basis of the speech component contained therein.
  • the audio signal 1 is first examined with an audio discriminator 3 to speech signal components 4 out. Found speech signal components 4 are forwarded for further processing, whereas other signal components such as music 5.1, pauses 5.2 or strong signal interference 5.3 can be sorted out and otherwise processed or discarded.
  • the audio signal is 1 piecewise, d. H. to bits a each about 100 ms to 500 ms, passed to the audio discriminator 3. This splits these bits further into individual buffers of about 20 ms in length, processes these buffers and then allocates them to one of the different signal groups speech signal, music, pause or strong interference.
  • the audio discriminator 3 uses, for example, an LPC (linear predictive coding) transformation for the evaluation of the signal chips, with which the coefficients of an adaptive filter corresponding to the human voice tract are calculated.
  • LPC linear predictive coding
  • a reference signal 6, d. H. a possible best estimate of the transmission signal originally transmitted by the transmitter, determined. This reference signal estimation takes place in several stages.
  • a noise suppression module 7 unwanted signal components such as stationary noise or impulse noise are first removed from the speech signal component 4 or suppressed. This is done with the aid of a neural network, which has been previously trained by means of a plurality of noisy signals as input and each of the corresponding noise-free version of the input signal as a target signal. The thus-obtained noisy voice signal 11 is forwarded to the second stage.
  • the interruption detection and interpolation module 8 interruptions in the audio signal 1 and in its voice signal component 4 are detected and interpolated if possible, d. H. the missing samples are replaced by appropriately estimated values.
  • the detection of signal interruptions by means of an examination of discontinuities of the signal fundamental frequency (pitch-tracing).
  • the interpolation is carried out as a function of the length of the detected interruption.
  • d. H. Interruptions of a few samples in length are polynomial-interpolated, such as Lagrange, Newton, Hermite, or Cubic Spline interpolation.
  • model-based interpolations such as maximum a posteriori, autoregressive, or frequency-time interpolation are used.
  • interpolation or other signal reconstruction is usually no longer possible in a meaningful way.
  • a terminal respond differently to missing frames. For example, in a first method, lost frames are simply replaced by zeros. In a second method, instead of the lost frames other, correctly received frames are used and in a third method locally generated noise signals, so-called "comfort noise" are used instead of the lost frames.
  • the speech signal component 4 After determining the reference signal 6 with the noise suppression module 7 and the interruption detection and interpolation module 8, it is compared with the speech signal component 4 with the aid of the comparison module 9.
  • an algorithm can be used, as used, for example, in intrusive methods for the comparison of the known source signal with the received signal.
  • psychoacoustic models that compare signals perceptually, ie perceptibly, are suitable.
  • the result of this comparison is an intrusive quality value 10.
  • the input signals, ie the speech signal component 4 and the reference signal 6 are decomposed into signal pieces of about 20 to 30 ms in length and a partial quality value is calculated for each signal piece. After about 20 to 30 signal pieces, which corresponds to a signal duration of 0.5 seconds, the intrusive quality value 10 is determined as the arithmetic mean of these partial quality values.
  • the intrusive quality value 10 forms the output signal of the comparison module 9.
  • a voice coder or speech decoder which the transmitted signal has passed through on its way from the transmitter to the receiver, can have an influence on the audio signal 1.
  • These influences include, for example, varying both the fundamental frequency and the higher harmonic frequencies of the signal. The smaller the bit rate of the speech codecs used, the greater the frequency shifts and thus the signal distortions.
  • Such influences can be examined most easily with vowels, which is why the noise signal 11 which has been deafened is first supplied to a vocal detector 12.
  • This includes, for example, a neural network that has been previously trained for the recognition of particular (single or all) vowels.
  • Vocal signals 13, d. H. Signal components that recognize the neural network as vowels are forwarded to an evaluation module 14, other signal components are discarded.
  • the evaluation module 14 divides the vocal signal 13 into signal pieces of about 30 ms and calculates thereon a DFT (discrete Fourier transform) with a frequency resolution of about 2 Hz at a sampling frequency of about 8 kHz. This allows the fundamental and higher harmonic frequencies to be determined and examined for variations. Another feature for evaluating codec-related distortions is the dynamics of the signal spectrum, with smaller dynamics implying poorer signal quality. The reference values for the dynamics evaluation are obtained for the individual vowels from example signals.
  • a codec quality value 15 is derived from the information about the influence of codecs on the frequency shifts and the spectrum dynamics of the audio signal 1 and of the denoised speech signal 11.
  • an interruption quality value 17 is taken into account in addition to the intrusive quality value 10 and the codec quality value 15.
  • This value contains information about the length and the number of interruptions detected by the interruption detection and interpolation module 8, whereby in a preferred embodiment of the invention only the information about the long interruptions is taken into account.
  • further quality information 18 about the received audio signal 1 or the noisy speech signal 11, which are determined with other modules or examinations, can be included in the calculations of the quality measure 2.
  • the individual quality values are now scaled to lie in the range of numbers between 0 and 1, where a quality value of 1 denotes undiminished quality and values below 1 indicate a correspondingly reduced quality.
  • the quality measure 2 is finally calculated as a linear combination of the individual quality values, wherein the individual weighting coefficients are determined experimentally and determined such that their sum is 1.
  • FIG. 2 shows the noise suppression module 7.
  • the voice signal portion 4 of the audio signal 1 is first subjected to a known DWT 19 (Discrete Wavelet Transformation).
  • DWT's are similar to DFT's used for signal analysis.
  • An essential difference, however, is the use of so-called wavelets, ie temporally limited and temporally localized waveforms with a mean value of 0, in contrast to the sinusoidal or cosine wave forms used indefinitely and thus not temporally localized in a DFT.
  • the speech signal component 4 is divided into signal pieces of about 20 ms to 30 ms, which are each subjected to the DWT 19.
  • the result of the DWT 19 is a set of DWT coefficients 20.1, which are input to a neural network 20 as an input vector. Its coefficients were previously trained to provide a new set of DWT coefficients 20.2 of the noisy version of this signal for a given set of DWT coefficients 20.1 of a noisy signal.
  • This new set of DWT coefficients 20.2 will now be sent to IDWT 21, i. H. subjected to the DWT 19 inverse DWT. In this way, this IDWT 21 delivers a majority of the unencumbered version of the speech signal components 4, namely the desired, denoised speech signal 11.
  • the training configuration of the neural network 20 is in FIG. 3 shown. It is trained with pairs of noisy and noisy versions of sample signals.
  • An unencumbered example signal 22.1 is subjected to the DWT 19 and a first set 20.3 of DWT coefficients is obtained.
  • the noisy sample signal 22.2 is also subjected to the same DWT 19 and a second set 20.4 of DWT coefficients is generated, which is fed into the neural network 20.
  • the output vector of the neural network 20, the new DWT coefficients 20.5 is compared in a comparator 23 with the first set 20.3 of DWT coefficients. Due to the differences between these two sets of DWT coefficients, there is a correction 24 of the coefficients of the neural network 20.
  • the training of the neural network 20 uses example signals 22.1, 22.2, which represent human sounds from different languages. It is also an advantage to use both female and male and child voices.
  • the mentioned size of the individually processed signal pieces of 20 ms to 30 ms duration is selected so that the processing of the speech signal component 4 can be performed independently of the speech and the speaker. Even pauses in speech and very quiet signal sections are trained so that they are recognized correctly.
  • each output neuron 27.1 provides one of the new DWT coefficients 20.2.
  • the audio discriminator 3 splits the signal bits into individual buffers of length 20 ms. At a sampling rate of 8 kHz, this corresponds to 160 samples.
  • a neural network 20 each having 160 input and output neurons 25.1, 27.1 and about 50 to 60 hidden neurons 26.1 may be used.
  • a time-frequency interpolation is used for the signal reconstruction.
  • a short-term spectrum for 64-sample-length (8 ms) signed frames is calculated. This is done by multiplying the signal frames by Hamming windows with a 50% overlap.
  • the goal of interpolation is to address this gap.
  • FIG. 5 shows such a signal 28 of about 200 samples in length.
  • FIG. 5 shows FIG. 5 the signal 28 in the temporal domain.
  • the abscissa axis 32 the number of samples and on the ordinate axis 33, the magnitudes are plotted.
  • the interpolation is done in the frequency-time domain.
  • the interruption 29 is easy to recognize as a gap of almost 10 samples.
  • a polynomial interpolation is now carried out for both the phase and magnitude, with minimal phase and magnitude discontinuity.
  • the pitch period 30 of the signal 28 is determined. Information from the samples before and after the gap within this pitch period 30 is taken into account for the interpolation.
  • the signal areas 31.1, 31.2 show those areas of the signal 28 each before a pitch period before or after the interruption 29. These signal areas 31.1, 31.2 are not identical to the original signal piece at the interruption 29, but still show a high degree of similarity , For small gaps up to about 10 samples, it is assumed that there is still enough signal information to be able to perform a correct interpolation. For longer gaps, additional information from samples of the environment can be used.
  • the invention allows the signal quality of a received audio signal to be assessed without knowing the original transmission signal. From the signal quality can of course be concluded on the quality of the transmission channels used and thus on the service quality of the entire telecommunications network.
  • the fast response times of the inventive method which are in the order of about 100 ms to 500 ms, thus allow various applications such as general comparisons of service quality of different networks or subnets, quality-based cost allocation or quality-based routing in a network or across multiple networks by means of appropriate control of network nodes (gateways, routers, etc.).

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Computational Linguistics (AREA)
  • Acoustics & Sound (AREA)
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  • Transmission Systems Not Characterized By The Medium Used For Transmission (AREA)
  • Detection And Prevention Of Errors In Transmission (AREA)
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  • Testing Electric Properties And Detecting Electric Faults (AREA)

Claims (10)

  1. Procédé pour la détermination, assistée par ordinateur, d'une mesure de qualité d'un signal audio, par lequel on détermine à partir du signal audio, un signal de référence qui représente une estimation d'un signal audio initialement émis, et par lequel l'on détermine, au moyen d'une comparaison du signal de référence au signal audio, une valeur de qualité qui est utilisée pour la détermination de la mesure de qualité, caractérisé en ce que l'on détermine un signal audio non bruité en éliminant des composantes de signal bruitées du signal audio et on l'utilise comme signal de référence, et en ce que l'on détecte dans le signal audio non bruité, des composantes de signal avec éléments vocaux, et l'on détermine des informations sur des distorsions de signal dues au codeur-décodeur et on prend en compte celles-ci lors de la détermination de la mesure de qualité.
  2. Procédé selon la revendication 1, caractérisé en ce que l'on détermine le signal audio non bruité en soumettant le signal audio à une transformation d'ondelettes discrète dont les coefficients sont introduits dans un réseau neuronal ayant subi auparavant un apprentissage et dont les signaux de sortie sont soumis à la transformation d'ondelettes discrète inverse.
  3. Procédé selon l'une des revendications 1 et 2, caractérisé en ce que l'on détecte des interruptions de signal dans le signal audio et en ce que l'on détermine le signal de référence en le reconstruisant au moins partiellement au niveau des interruptions de signal, le signal de référence étant reconstruit de préférence avec une interpolation polynomiale en cas d'interruptions de signal courtes et de préférence avec une interpolation basée sur un modèle en cas d'interruptions de signal moyennement longues.
  4. Procédé selon la revendication 3, caractérisé en ce que, lors de la détermination de la mesure de qualité, on prend en compte des informations sur les interruptions de signal.
  5. Procédé selon l'une des revendications 1 à 4, caractérisé en ce que, avant la détermination du signal de référence, on extrait une composante de signal vocale du signal audio, et en ce qu'on limite la détermination de la mesure de qualité à la composante de signal vocale.
  6. Dispositif pour la détermination d'une mesure de qualité, lequel présente des premiers moyens pour déterminer un signal de référence à partir du signal audio, des deuxièmes moyens pour déterminer une valeur de qualité au moyen d'une comparaison du signal de référence au signal audio et des troisièmes moyens pour déterminer la mesure de qualité en tenant compte de la valeur de qualité, grâce à quoi le signal de référence représente une estimation d'un signal audio émis à l'origine, caractérisé en ce qu'il présente des moyens pour éliminer du signal audio des composantes de signal bruitées et des moyens pour déterminer des distorsions de signal dues au codeur-décodeur, celles-ci comportant un module de détection d'éléments vocaux pour détecter des composantes de signal avec éléments vocaux dans le signal audio non bruité, ainsi qu'un module d'évaluation pour déterminer les distorsions de signal provoquées par le codeur-décodeur, les troisièmes moyens étant conçus de sorte que les distorsions de signal causées par le codeur-décodeur puissent être prises en considération lors de la détermination de la mesure de qualité.
  7. Dispositif selon la revendication 6, caractérisé en ce que les premiers moyens comportent un module de suppression de bruit pour supprimer des composantes de signal bruitées et/ou un module de détection d'interruptions et d'interpolation pour détecter et interpoler des interruptions de signal dans le signal audio, et en ce que les troisièmes moyens sont conçus de telle sorte que des interruptions de signal peuvent être prises en compte lors de la détermination de la mesure de qualité.
  8. Dispositif selon l'une des revendications 6 et 7, caractérisé en ce qu'il comporte des moyens pour extraire du signal audio une composante de signal vocale, et en ce qu'il est conçu pour la détermination de la mesure de qualité de la composante de signal vocale.
  9. Dispositif selon la revendication 7, les premiers moyens comportant le module de suppression de bruit, caractérisé en ce que le module de suppression de bruit comporte des moyens pour la mise en oeuvre d'une transformation d'ondelettes discrète en vue du calcul de coefficients de signal d'un signal audio, un réseau neuronal en vue du calcul de coefficients de signal corrigés, ainsi que des moyens pour la mise en oeuvre d'une transformation d'ondelettes inverse des coefficients de signal corrigés en vue de la détermination du signal audio sans composantes de signal bruitées.
  10. Dispositif selon la revendication 7, les premiers moyens comportant le module de détection d'interruptions et d'interpolation, caractérisé en ce que le module de détection d'interruptions et d'interpolation comporte des moyens pour détecter des interruptions de signal dans un signal audio ainsi que des moyens pour interpoler des interruptions du signal audio, ces derniers étant conçus de préférence pour une interpolation polynomiale d'interruptions de signal courtes et pour une interpolation, basée sur un modèle, d'interruptions de signal moyennement longues.
EP02703438.8A 2001-03-20 2002-03-19 Procede et dispositif pour determiner un niveau de qualite d'un signal audio Expired - Lifetime EP1386307B2 (fr)

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EP02703438.8A EP1386307B2 (fr) 2001-03-20 2002-03-19 Procede et dispositif pour determiner un niveau de qualite d'un signal audio

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EP01810285 2001-03-20
EP01810285A EP1244094A1 (fr) 2001-03-20 2001-03-20 Procédé et dispositif de détermination de la qualité d'un signal audio
PCT/CH2002/000164 WO2002075725A1 (fr) 2001-03-20 2002-03-19 Procede et dispositif pour determiner un niveau de qualite d'un signal audio
EP02703438.8A EP1386307B2 (fr) 2001-03-20 2002-03-19 Procede et dispositif pour determiner un niveau de qualite d'un signal audio

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EP1386307B1 EP1386307B1 (fr) 2005-02-09
EP1386307B2 true EP1386307B2 (fr) 2013-04-17

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AT (1) ATE289109T1 (fr)
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WO (1) WO2002075725A1 (fr)

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ATE289109T1 (de) 2005-02-15
DE50202226D1 (de) 2005-03-17
EP1386307A1 (fr) 2004-02-04
EP1244094A1 (fr) 2002-09-25
US6804651B2 (en) 2004-10-12
WO2002075725A1 (fr) 2002-09-26
EP1386307B1 (fr) 2005-02-09

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