EP2158588B1 - Procédé de filtrage spectral de signaux parasités - Google Patents

Procédé de filtrage spectral de signaux parasités Download PDF

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Publication number
EP2158588B1
EP2158588B1 EP08784249A EP08784249A EP2158588B1 EP 2158588 B1 EP2158588 B1 EP 2158588B1 EP 08784249 A EP08784249 A EP 08784249A EP 08784249 A EP08784249 A EP 08784249A EP 2158588 B1 EP2158588 B1 EP 2158588B1
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Prior art keywords
short
smoothing method
transformation
smoothing
term
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German (de)
English (en)
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EP2158588A1 (fr
Inventor
Rainer Martin
Timo Gerkmann
Colin Breithaupt
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Sivantos GmbH
Ruhr Universitaet Bochum
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Siemens Audioligische Technik GmbH
Ruhr Universitaet Bochum
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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
    • 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
    • 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
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/24—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being the cepstrum
    • 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
    • G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique

Definitions

  • the invention relates to a smoothing method for suppressing fluctuating artifacts in noise reduction.
  • noise suppression is an important aspect.
  • the audio signals recorded with a microphone and subsequently digitized contain, in addition to the useful signal ( FIG. 1 ) still ambient noise, which are superimposed on the useful signal ( FIG. 2 ).
  • FIG. 1 the useful signal
  • FIG. 2 the useful signal
  • hearing aids are constantly changing ambient noise such as traffic noise or people talking in the background, such as in a restaurant.
  • the noise reduction aims accordingly to a relief of Speech understanding. Therefore, reducing the noise should not audibly distort the speech signal.
  • the spectral representation is a favorable representation of the signal.
  • the signal is displayed in frequency broken down.
  • a practical realization of the spectral representation are short-term spectra which result from a division of the signal into short frames ( FIG. 3 ), which are subjected to a spectral transformation separately from each other ( FIG. 4 ).
  • a transformed frame then consists of M so-called frequency bins.
  • the squared amplitude value of a frequency bin corresponds to the energy that contains the signal in the narrow frequency slice of about 31 Hz bandwidth represented by the respective frequency bin.
  • 129 bins Due to the symmetry properties of the spectral transformation, only M / 2 + 1 of the M frequency bins, ie 129 bins in the previous example, are relevant for the signal representation. With 129 relevant bins and 31 Hz bandwidth per bin, a total of spectral band from 0 Hz to about 4000 Hz is covered. This is sufficient to describe many speech sounds with sufficient spectral resolution. Another common bandwidth is 8000 Hz, which can be achieved by a higher sampling rate and thus more frequency bins with the same frame duration.
  • the frequency bins are indexed with ⁇ . The index for frames is ⁇ .
  • the amplitudes of the short-term spectrum of a frame ⁇ are generally noted here as a spectral quantity G ⁇ ( ⁇ ).
  • a common form of presentation of short-term spectra are so-called spectrograms, which are formed by juxtaposing temporally successive short-term spectra (cf., for example FIGS. 6 to 9 ).
  • the advantage of the spectral representation is that the essential speech energy is concentrated in a relatively small number of frequency bins ( FIGS. 4 and 6 ), while in the time signal all digital samples are equally relevant ( FIG. 3 ).
  • the signal energy of the disturbance is in most cases distributed over a larger number of frequency bins. Since the frequency bins contain different amounts of speech energy, it is possible to suppress the noise in those bins that contain little speech energy. The narrowband the frequency bins are, the better this separation succeeds.
  • a spectral weighting function is estimated, which can be calculated according to different optimization criteria. It results in low values or zero in frequency bins that are predominantly perturbing, and values close to or equal to one for bins in which voice energy dominates ( FIG. 5 ).
  • the weighting function is generally re-estimated for each signal frame in each frequency bin.
  • the totality of the weighting values of all the frequency bins of a frame is also referred to herein as the "short-term spectrum of the weighting function" or simply as the "weighting function".
  • Multiplication of the weighting function with the short-term spectrum of the noisy signal yields the filtered spectrum in which the amplitudes of the frequency bins in which interference dominates are greatly reduced, while speech components remain almost unaffected ( FIGS. 8 and 9 ).
  • tonal artifacts musical noise
  • FIGS. 10 and 11 A single tonal artifact has the duration of a signal frame and its frequency is determined by the frequency bin in which the outlier occurred.
  • these spectral magnitudes can be smoothed by an averaging method and thus freed from excessive values.
  • Spectral magnitudes of several spectrally adjacent or temporally successive frequency bins are calculated to an average, so that the amplitude of individual outliers is relativized. Smoothing is above the frequency [1: Tim Fingscheidt, Christophe Beaugeant and Suhadi Suhadi. Overcoming the statistical independence assumption wrt frequency in speech enhancement. Proceedings, IEEE Int. Conf.
  • the cepstrum principally consists of a non-linear mapping, namely the logarithmization, a spectral value of magnitude and a subsequent transformation of this logarithmic magnitude spectrum with a transformation.
  • the advantage of a cepstral representation of the amplitudes is that speech is no longer comb-like across the frequency ( FIGS. 4 and 6 ), but the essential information about the speech signal is represented in the cepstral small index bins. In addition, substantial speech information is still represented in the relatively easily detectable cepstral bin of higher index, which represents the so-called pitch frequency of the speaker.
  • a smoothed short-term spectrum can be calculated by setting cepstral bins to zero with relatively small amounts, and then re-transforming the altered cepstrum back to a short-term spectrum.
  • cepstral bins to zero with relatively small amounts
  • re-transforming the altered cepstrum back to a short-term spectrum.
  • strong fluctuations or outliers to correspondingly high Amplitudes in the cepstrum these artifacts can not be detected by these methods and suppressed.
  • the object of the invention is to provide a smoothing method for suppressing fluctuations in the weighting function or in spectral intermediate variables or outliers in filtered short-term spectra, which neither reduces the frequency resolution of the short-term spectra nor impairs the temporal dynamics of the speech signal.
  • the smoothing method according to the invention makes use of a transformation such as cepstrum in order to describe a broadband speech signal with as few transformation coefficients as possible in its essential structure.
  • the transformation coefficients are not set to zero independently of each other if they fall below a threshold value. Instead, the values of transformation coefficients from at least two consecutive frames are offset by smoothing over time.
  • the degree of smoothing is made dependent on the extent to which the spectral structure represented by the coefficient is decisive for the description of the useful signal.
  • the degree of temporal smoothing of a coefficient therefore depends, for example, on whether a transformation coefficient contains a lot of speech energy or little. This is easier to determine in cepstrum or similar transformations than in the short-term spectrum.
  • Coefficients with a large amount of speech information are only smoothed to the extent that their temporal dynamics do not become lower than with an unbounded speech signal. If necessary, these coefficients are not even smoothed. Speech distortions are thus prevented. Since spectral fluctuations and outliers represent a short-term change in the fine structure of a short-term spectrum, they form in the transformed short-term spectrum as a short-term change of those transformation coefficients that represent the fine structure of the short-term spectrum.
  • G ⁇ ' smooth cepst ⁇ .
  • G ⁇ ' smooth cepst ⁇ .
  • Transformations differ in their basic functions used.
  • the process of transformation means that the signal is correlated with the various basis functions.
  • the resulting degree of correlation between the signal and a basis function is then the associated transformation coefficient.
  • Orthogonal transformation bases contain only basis functions that are uncorrelated. In the case that the signal is identical to one of the basic functions, transform coefficients with the value zero are produced in the case of orthogonal transformations, with the exception of the one coefficient which is identical to the signal. The selectivity of an orthogonal transformation is therefore high.
  • Non-orthogonal transforms use function bases that are correlated with each other.
  • Another feature is that the basic functions for the considered application are discrete and finite, since the processed signal frames are discrete signals of the length of one frame.
  • DFT Discrete Fourier Transform
  • FFT Fast Fourier Transform
  • DCT discrete cosine transform
  • DST discrete sine transform
  • the invertibility of the transforms also makes it possible to interchange the transform and its inverse in the back and forth transformations.
  • the use of the DFT from (2) is thus also possible, for example, if the IDFT from (1) is used in (2).
  • the spectral coefficients of the short-term spectra are mapped before non-linear transformation.
  • a principal feature of non-linear imaging which is advantageous for the invention is dynamic compression of relatively large amplitudes and dynamic expansion of relatively small amplitudes.
  • the spectral coefficients of the smoothed short-term spectra after the inverse transformation can be mapped non-linearly, the non-linear mapping after the inverse transformation being the inverse of the non-linear mapping before the forward transformation.
  • the spectral coefficients are imaged non-linearly by logarithmization before the forward transformation.
  • the smoothing method is applied to the magnitude or power of the magnitude of the short-term spectra.
  • time constants can be chosen in such a way that the transformation coefficients, which primarily represent language, are less well smoothed.
  • the transformation coefficients, the mainly fluctuating background noise and Artifacts of noise reduction algorithms describe being heavily smoothed.
  • the spectral weighting function of a noise reduction algorithm can be provided.
  • the spectral weighting function of a postfilter for multi-channel noise reduction methods can also be used as the short-term spectrum.
  • the spectral weighting function results here from the minimization of an error criterion.
  • a filtered short-term spectrum can also be provided.
  • a spectral weighting function of a multi-channel method for noise reduction is provided as the short-term spectrum.
  • an estimated coherence or an estimated magnitude squared coherence can also be provided between at least two microphone channels.
  • a spectral weighting function of a multi-channel method for speaker or source separation is provided as the short-term spectrum.
  • a spectral weighting function of a multi-channel method for speaker separation on the basis of phase differences of signals in the different channels is provided as a short-term spectrum.
  • GCC generalized cross-correlation
  • spectral magnitudes containing both speech and noise components can also be provided.
  • an estimate of the signal-to-noise ratio in the individual frequency bins can also be provided as the short-term spectrum. Further, as the short-term spectrum, an estimate of the noise power can be used.
  • the line of an image is interpreted as a signal frame that can be transformed into the spectral range.
  • the resulting frequency bins are called spatial frequency bins here.
  • algorithms equivalent to those used in audio signal processing are used. Possible fluctuations that generate these algorithms in the spatial frequency range result in the processed image in optical artifacts. These are equivalent to tonal noise in audio processing.
  • signals are derived from the human body that can be noisy, such as acoustic signals.
  • the noisy signal can be transformed into the spectral range frame by frame.
  • the resulting spectrograms can be processed like audio spectra.
  • the smoothing method can be used in a telecommunications network and / or in broadcasting to improve speech and / or picture quality and to suppress artifacts.
  • the speech coding redundancy-reducing speech compression
  • the associated quantization noise and on the other by the interference caused by the transmission channel.
  • the latter fluctuate greatly in terms of time and spectral and lead to a noticeable deterioration of the voice quality.
  • the receiver side or network used Signal processing ensure that the quasi-random artifacts are reduced.
  • so-called post-filters and error concealment methods have hitherto been used.
  • the smoothing method can thus be used as a postfilter, in a postfilter, in combination with a postfilter, as part of an error concealment method or in connection with a method for speech and / or picture coding (decompression method or decoding method), in particular on the receiver side.
  • a postfilter it is meant that the method is used for post-filtering, that is to say that the data produced in the applications are processed with an algorithm implementing the method.
  • FIG. 1 is an unencumbered signal in the form of amplitude over time.
  • the duration of the signal is 4 seconds, the amplitudes range from about -0.18 to about 0.18.
  • FIG. 2 the signal is shown in noisy form. One recognizes a random background noise over the entire time course.
  • FIG. 3 the signal of a single signal frame ⁇ is shown.
  • the signal frame has a segment duration of 32 milliseconds.
  • the amplitude of both graphs ranges between -0.1 and 0.1.
  • the individual samples of the digital signals are connected to graphs.
  • the noisy graph represents the input signal containing the noisy signal. A separation of signal and noise in the noisy signal is hardly possible in this representation of the signal.
  • FIG. 4 is a representation of the same signal frame after the transformation into the frequency domain.
  • the individual frequency bins ⁇ are connected to graphs.
  • the frequency bins are noisy and noisy, again with the noisy signal in the noisy signal is included speech signal.
  • the abscissa shows the frequency bins ⁇ from 0 to 128. They have amplitudes of about -40 decibels (dB) to about 10 dB. From the comparison of the graphs, it can be seen that the energy of the speech signal in some frequency bins is concentrated in a comb-like structure, while the noise is also present in the intervening bins.
  • FIG. 5 is a weighting function for the noisy frame FIG. 4 shown.
  • a factor between 0 and 1 results depending on the ratio of speech and noise energy.
  • the individual weighting factors are connected to a graph. One recognizes the comb-like structure of the speech spectrum again.
  • FIGS. 6 and 7 are spectrograms from a series of noisy or noisy short-term spectra ( FIG. 4 ).
  • the frame index ⁇ is plotted, on the ordinate of the frequency bin index ⁇ .
  • the amplitudes of the individual frequency bins are shown as gray values.
  • FIGS. 6 and 7 It becomes clear how language is concentrated in a few frequency bins. It also trains regular structures. The noise, however, is distributed over all frequency bins.
  • FIG. 8 the spectrogram of a filtered signal is shown.
  • the axes correspond to those from the FIGS. 6 and 7 . From a comparison with FIG. 6 It can be seen that estimation errors in the weighting function leave high amplitudes in frequency bins which contain no speech. To suppress these outliers is the aim of the method according to the invention.
  • FIG. 9 is the spectrogram of a signal is shown, which has been filtered according to a preferred embodiment of the method according to the invention with a smoothed weighting function.
  • the axes correspond to those of the previous spectrograms.
  • the outliers are greatly reduced.
  • the speech components in the spectrogram are preserved in their essential form.
  • FIGS. 10 and 11 the time signals are shown, each resulting from the filtered spectra of the FIGS. 8 and 9 result. Plotted is the amplitude over time. The signals are 4 seconds long and have amplitudes between about -0.18 and 0.18.
  • the outliers in the spectrogram out FIG. 8 result in the associated time signal in FIG. 10 clearly visible tonal artifacts, which in the noisy signal FIG. 1 are not available.
  • the time signal in FIG. 11 has a much quieter course of residual noise. This time signal results from the spectrogram of FIG. 9 that was generated by filtering with the smoothed weighting function.
  • the unsmoothed weighting function is shown for all frames. Frequency bins ⁇ are plotted along the ordinate for each frame ⁇ . The values of the weighting function are shown as gray tones. The fluctuations resulting from estimation errors are recognizable as irregular patches.
  • FIG. 13 the smoothed weighting function is shown for all frames.
  • the axes correspond to those from FIG. 12 . Due to the smoothing, the fluctuations are smeared and greatly reduced in value. The structure of the voice frequency bins, however, remains clearly recognizable.
  • FIG. 14 is the amount of the cepstrum of an unbroken signal across all frames.
  • the cepstral bins ⁇ ' are plotted along the ordinate.
  • the values of the amounts of cepstral coefficients G ⁇ ' cepst ⁇ are shown as shades of gray.
  • a comparison with FIG. 6 shows that language in cepstrum is concentrated on an even smaller number of coefficients. In addition, these coefficients are less variable in their position.
  • Clearly recognizable is the course of the cepstral coefficient, which represents the pitch frequency.
  • FIG. 15 a signal flow graph according to a preferred embodiment of the invention is shown.
  • a noisy input signal is transformed into a sequence of short-term spectra, and then a weighting function for filtering is then estimated via spectral intermediate quantities. It will be respectively a frame currently being edited.
  • the short-term spectra of the weighting function are subjected to a non-linear, logarithmic mapping. This is followed by a transformation into the cepstral area.
  • the thus transformed short-term spectra are thus represented by transformation coefficients of the basis functions.
  • the transformation coefficients calculated in this way are smoothed separately using different time constants.
  • the recursive nature of the smoothing is indicated by the return of the output of the smoothing to its input.

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Claims (34)

  1. Procédé de lissage pour supprimer des artefacts fluctuants lors de la réduction du bruit parasite, comprenant les stades suivants :
    • on se procure des spectres de temps court d'une succession de trames de signal comprenant des valeurs numériques d'échantillonnage,
    • on transforme chaque spectre de temps court par une transformation, qui décrit le spectre de temps court par des coefficients de transformation, qui représentent le spectre de temps court subdivisé en ses structures grossières et ses structures fines,
    • on lisse les coefficients de transformation respectivement de même indice de coefficient par combinaison d'au moins deux spectres de temps court successifs transformés, et
    • on transforme par une retransformation les coefficients de transformation lissés en des spectres de temps court lissés.
  2. Procédé de lissage suivant la revendication précédente, caractérisé en ce qu'on utilise l'inverse de la transformation pour la retransformation.
  3. Procédé de lissage suivant la revendication 1 ou 2, caractérisé en ce qu'on utilise une transformation de base orthogonale.
  4. Procédé de lissage suivant la revendication 1 ou 2, caractérisé en ce qu'on utilise une transformation de base non orthogonale.
  5. Procédé de lissage suivant la revendication 1 ou 2, caractérisé en ce qu'on utilise pour la transformation la transformation de Fourier discrète et son inverse.
  6. Procédé de lissage suivant la revendication 1 ou 2, caractérisé en ce qu'on utilise pour la transformation la transformation de Fourier rapide et son inverse.
  7. Procédé de lissage suivant la revendication 1 ou 2, caractérisé en ce qu'on utilise pour la transformation la transformation cosinus discrète et son inverse.
  8. Procédé de lissage suivant la revendication 1 ou 2, caractérisé en ce qu'on utilise pour la transformation la transformation sinus discrète et son inverse.
  9. Procédé de lissage suivant l'une des revendications précédentes, caractérisé en ce qu'on reproduit de manière non linéaire les spectres de temps court avant la transformation.
  10. Procédé de lissage suivant la revendication précédente, caractérisé en ce qu'on reproduit de manière non linéaire, après la retransformation, les spectres de temps court lissés, la reproduction non linéaire de la retransformation étant l'inverse de la reproduction non linéaire de la transformation.
  11. Procédé de lissage suivant l'une des deux revendications précédentes, caractérisé en ce qu'on reproduit de manière non linéaire en prenant le logarithme les spectres de temps court avant la transformation.
  12. Procédé de lissage suivant l'une des revendications 1 à 11, caractérisé en ce qu'on utilise un lissage récursif pour le lissage des coefficients de transformation.
  13. Procédé de lissage suivant l'une des revendications 1 à 11, caractérisé en ce qu'on utilise un lissage non récursif pour le lissage des coefficients de transformation.
  14. Procédé de lissage suivant l'une des revendications précédentes, caractérisé en ce qu'on applique le lissage à la valeur absolue ou à une puissance de la valeur absolue des spectres de temps court.
  15. Procédé de lissage suivant l'une des revendications précédentes, caractérisé en ce qu'on utilise pour le lissage des coefficients de transformation respectifs des constantes de temps différentes.
  16. Procédé de lissage suivant la revendication précédente, caractérisé en ce qu'on choisit des constantes de temps de manière à lisser peu les coefficients de transformation, qui décrivent typiquement des structures spectrales de parole.
  17. Procédé de lissage suivant l'une des revendications précédentes, caractérisé en ce qu'on choisit des constantes de temps de manière à lisser beaucoup les coefficients de transformation, qui décrivent des structures spectrales de grandeurs spectrales fluctuantes et d'artéfacts d'algorithmes de réduction de bruit.
  18. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une fonction spectrale de pondération d'un algorithme de réduction de bruit.
  19. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une fonction spectrale de pondération d'un post-filtre pour des procédés à plusieurs canaux de réduction du bruit.
  20. Procédé de lissage suivant l'une deux revendications précédentes, caractérisé en ce qu'on obtient la fonction spectrale de pondération à partir de la minimisation d'un critère d'erreur.
  21. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court un spectre de temps court filtré.
  22. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une fonction spectrale de pondération d'un procédé à plusieurs canaux de réduction du bruit.
  23. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une cohérence estimée ou une "Magnitude Squared Coherence" estimée entre au moins deux microcanaux radio.
  24. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une fonction spectrale d'un procédé à plusieurs canaux pour la séparation de locuteurs ou de sources.
  25. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une fonction spectrale d'un procédé à plusieurs canaux pour la séparation de locuteurs sur la base de différences de phase de signaux dans les divers canaux ( phase Transform - PHAT ).
  26. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une fonction spectrale de pondération d'un procédé à plusieurs canaux pour la réduction du bruit sur la base d'une "Generalized Cross-Correlation" (GCC).
  27. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court des grandeurs spectrales, qui contiennent à la fois des parties vocales et des parties parasites.
  28. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une évaluation du rapport du signal au bruit.
  29. Procédé de lissage suivant l'une des revendications 1 à 17, caractérisé en ce qu'on se procure comme spectre de temps court une évaluation de la puissance du bruit.
  30. Procédé de lissage suivant l'une des revendications 1 à 15, caractérisé en ce qu'on se procure comme spectre de temps court des trames transformées d'un signal d'image et on soumet les coefficients ligne par ligne ou colonne par colonne ou calculés en deux dimensions du signal d'image transformé à un lissage dans l'espace par des paramètres de lissage différents.
  31. Procédé de lissage suivant la revendication précédente, caractérisé en ce que le signal d'image est un signal vidéo.
  32. Procédé de lissage suivant l'une des revendications 1 à 15, caractérisé en ce qu'on utilise comme spectre de temps court un signal médical transformé dérivé du corps humain.
  33. Procédé de lissage suivant l'une des revendications 1 à 32, caractérisé en ce qu'on utilise le procédé de lissage dans un post-filtre, en combinaison avec un post-filtre, dans le cas d'un procédé de découverte d'erreur ou en liaison avec un procédé de codage de la voix et/ou de l'image, notamment du côté récepteur.
  34. Procédé de lissage suivant l'une des revendications 1 à 33, caractérisé en ce qu'on utilise le procédé de lissage dans un réseau de télécommunication et/ou dans une radiodiffusion pour améliorer la qualité de la voix et/ou de l'image, ainsi que pour supprimer des artefacts.
EP08784249A 2007-06-27 2008-06-25 Procédé de filtrage spectral de signaux parasités Not-in-force EP2158588B1 (fr)

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DE102007030209A DE102007030209A1 (de) 2007-06-27 2007-06-27 Glättungsverfahren
PCT/DE2008/001047 WO2009000255A1 (fr) 2007-06-27 2008-06-25 Procédé de filtrage spectral de signaux parasités

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ATE484822T1 (de) 2010-10-15
DK2158588T3 (da) 2011-02-07
WO2009000255A1 (fr) 2008-12-31
US8892431B2 (en) 2014-11-18
EP2158588A1 (fr) 2010-03-03
US20100182510A1 (en) 2010-07-22
DE102007030209A1 (de) 2009-01-08
DE502008001543D1 (de) 2010-11-25

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