EP1091349B1 - Procédé et dispositif pour la réduction de bruit durant la transmission de parole - Google Patents

Procédé et dispositif pour la réduction de bruit durant la transmission de parole Download PDF

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Publication number
EP1091349B1
EP1091349B1 EP00250301A EP00250301A EP1091349B1 EP 1091349 B1 EP1091349 B1 EP 1091349B1 EP 00250301 A EP00250301 A EP 00250301A EP 00250301 A EP00250301 A EP 00250301A EP 1091349 B1 EP1091349 B1 EP 1091349B1
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Prior art keywords
film
reaction
signal
noise
minima
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German (de)
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EP1091349A3 (fr
EP1091349A2 (fr
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Dietmar Dr. Ruwisch
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RUWISCH, DIETMAR, DR.
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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
    • G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
    • G10L25/30—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks

Definitions

  • the invention relates to a method for noise suppression in speech transmission by multiplication of spectra of a speech signal with a filter, which by arithmetic operations on spectrums of the input signal with the help of a multi-layered, self-organizing, to determine the feedback neural network is.
  • LPC requires the elaborate Calculation of correlation matrices to use with the help of a linear prediction method filter coefficients too calculate, as from T. Arai, H. Hermansky, M. Paveland, C. Avendano, Intelligence of Speech with Filtered Time Trajectories of LPC Cepstrum, The Journal of the Acoustical Society of Maerica, Vol. 4, Pt. 2, p. 2756, 1996, is known.
  • US Pat. No. 5,878,389 A discloses a method for noise suppression in speech signals, which is based on a smoothing of short-term spectra over time and frequency.
  • the smoothing can be carried out by a neural network.
  • the signal spectrum itself is subjected to the smoothing, which always has a negative effect on the voice quality.
  • Object of the present invention is to provide a method that recognizes a speech signal in terms of its temporal and spectral characteristics with little computational effort and can be freed from noise.
  • This task is solved by a minimadetection layer, which minima over past signal spectra detected, a reaction layer, which a non-linear response to that of the minimadetection layer detects detected minima, one Diffusion layer, which with only local couplings adjacent compute node in the diffusion layer a spectral smoothing on the outputs of the reaction layer performs, an integration layer, which the Output of the diffusion layer without weighting in one Compute nodes added up, with a filter function F (f, T) for noise filtering is created by coupling of compute nodes of successive layers Minimadetektionstik, reaction layer, diffusion layer and integration layer, where f is the frequency a spectral component, which for Time T is to analyze.
  • Such a method recognizes a speech signal on its temporal and spectral properties and frees this from noise. Compared to known methods, the required computational effort is low.
  • the procedure is characterized by a particularly short adaptation time, within which the system on the type of noise. The signal delay when processing the signal very short, so that the filter in real time for telecommunications is operational.
  • the invention also relates to a device For noise suppression according to claim 9. Further advantageous measures are in the Subclaims described.
  • the invention is in the enclosed.
  • FIG 1 is schematically and exemplarily an overall system presented for language filtering. This consists from a sampling unit 10, which is the noisy one Speech signal in the time t samples and discretizes and thus generates samples x (t) that are in time T to frames from n samples are summarized.
  • a sampling unit 10 which is the noisy one Speech signal in the time t samples and discretizes and thus generates samples x (t) that are in time T to frames from n samples are summarized.
  • FIG. 2 shows a minimadetection layer, a reaction layer, a diffusion layer and a Integration layer containing neural network, which is in particular the subject of the invention and to which the spectrum A (f, T) of the input signal is supplied from which the filter function F (f, T) is calculated becomes.
  • Each of the modes of the spectrum that goes through distinguish the frequency f corresponds to one single neuron per layer of the network except the integration layer.
  • the individual layers become specified in the following figures.
  • FIG. 3 shows a neuron of the minima detection layer, which determines M (f, T).
  • M (f, T) is in fashion with frequency f the minimum of over m frames averaged Amplitude A (f, T) within an interval of Time T, which corresponds to the length of 1 frame.
  • FIG. 4 shows a neuron of the reaction layer which by means of a reaction function r [S (T-1)] from the integral signal S (T-1), as shown in detail in FIG is shown, and a freely selectable parameter K, which determines the degree of noise suppression, from A (f, T) and M (f, T) determines the relative spectrum R (f, T).
  • R (f, T) has a value between zero and one.
  • the Reaction layer distinguishes speech from noise based on the temporal behavior of the signal.
  • FIG. 5 shows a neuron of the diffusion layer in which a diffusion corresponding local coupling between the fashions is made.
  • the diffusion constant D determines the strength of the resulting Smoothing over the frequencies f at fixed time T.
  • the diffusion layer determined from the relative signal R (f, T) the actual filter function F (f, T), with the the spectrum A (f, T) is multiplied to noise to eliminate.
  • In the diffusion layer is Language of sounds based on spectral properties distinguished.
  • Figure 6 shows that in the chosen embodiment of the invention only neuron of the integration layer that the Filter function F (f, T) at fixed time T over the frequencies f integrated and the integral signal thus obtained S (T) is fed back into the reaction layer, as Figure 2 shows.
  • This global coupling ensures that is heavily filtered at high noise while noise-free speech is transmitted unadulterated.
  • FIG. 7 shows exemplary details of the filter properties the invention for various settings of the control parameter K.
  • the picture shows the Damping of amplitude modulated white noise in Dependence of the modulation frequency. At modulation frequencies between 0.6 Hz and 6 Hz is the attenuation less than 3 dB. This interval corresponds to the typical modulation of human speech.
  • a filter unit 11 is from the Spectrum A (f, T) produces a filter function F (f, T) and multiplied by the spectrum. This gives you that filtered spectrum B (f, T), from which in a synthesis unit by inverse Fourier transform the noise-freed Speech signal y (t) is generated. This can after digital-to-analog conversion in a speaker be made audible.
  • the filter function F (f, T) is a neural Network, which is a minimadetection layer, a reaction layer, a diffusion layer and a Contains integration layer, as Figure 2 shows.
  • the spectrum A (f, T) generated by the sampling unit 10 is first fed to the Minimadetektions layer, as it shows the figure 3.
  • a single neuron of this layer works independently from the other neurons of the minimadetection layer a single mode, represented by the frequency f is marked.
  • the neuron averages for this fashion the amplitudes A (f, T) in the time T over m frames. From the neuron then determines these average amplitudes over a period of time in T, which is 1 frame in length corresponds to the minimum for his fashion.
  • the signal M (f, T) which is then fed to the reaction layer becomes.
  • every neuron of the reaction layer processes a single mode of frequency f, independent of the other neurons in this layer.
  • all neurons will be externally adjustable Paramter K, whose size is the degree of Noise suppression of the entire filter is determined additionally the integral signal S (T-1) stands for these neurons from the previous frame (time T-1), the in the integration layer, as shown in FIG. 6 has been.
  • This signal is the argument of a nonlinear reaction function r, with whose help the neurons of the reaction layer the relative spectrum R (f, T) at the time T calculate.
  • the value range of the reaction function is on an interval [r1, r2] restricted.
  • the value range of the in this way resulting relative spectrum R (f, T) is limited to the interval [0, 1].
  • reaction layer is the temporal behavior of the speech signal for distinguishing useful and Interference signal evaluated.
  • Spectral properties of the speech signal are in the Diffusion layer, as shown in FIG 5, evaluated, whose neurons have a local mode coupling according to Art perform a diffusion in the frequency space.
  • the item has the invention no frequency response in the conventional Sense.
  • the filter characteristics influence.
  • a suitable method for analyzing the properties the filter uses an amplitude modulated noise signal, in dependence on the modulation frequency the To determine damping of the filter, as the figure 7 shows. To do this, set the input and output side mean integral performance in relation to each other and carries this value against the modulation frequency of the Test signal on. In Figure 7, this "modulation path" for different values of the control parameter K shown.

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  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Human Computer Interaction (AREA)
  • Quality & Reliability (AREA)
  • Computational Linguistics (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Reduction Or Emphasis Of Bandwidth Of Signals (AREA)
  • Telephone Function (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)
  • Noise Elimination (AREA)
  • Transmission Systems Not Characterized By The Medium Used For Transmission (AREA)

Claims (13)

  1. Procédé de réduction du bruit durant la transmission de parole par multiplication de spectres d'un signal vocal avec un filtre, lequel est déterminé par des opérations de calcul sur les spectres du signal d'entrée à l'aide d'un réseau neuronal à plusieurs couches auto-organisant à rétroaction, caractérisé par
    une couche de détection de minima qui détecte des minima sur des spectres de signal précédents,
    une couche de réaction qui exécute une fonction de réaction non linéaire sur les minima détectés par la couche de détection de minima,
    une couche de diffusion qui exécute un lissage spectral sur les sorties de la couche de réaction avec seulement des couplages locaux de noeuds de calcul voisins dans la couche de diffusion,
    une couche d'intégration qui cumule dans un noeud de calcul la sortie de la couche de diffusion sans pondération,
    une fonction de filtrage F(f, T) pour le filtrage du bruit étant obtenue par couplage de noeuds de calcul des couches successives couche de détection de minima, couche de réaction, couche de diffusion et couche d'intégration, f désignant la fréquence d'une composante spectrale qui est à analyser à l'instant T.
  2. Procédé selon la revendication 1, caractérisé par le fait qu'il faut multiplier un paramètre réglable K par la fonction de réaction dans la couche de réaction pour définir l'intensité de la réduction de bruit du filtre dans son ensemble.
  3. Procédé selon la revendication 1 ou 2, caractérisé par le fait qu'un noeud de calcul de la couche d'intégration intègre la fonction de filtrage F (f, T) à un instant fixe T sur les fréquences f en une valeur S(T) qui est à réinjecter dans la couche de réaction.
  4. Procédé selon l'une des revendications 1 à 3, caractérisé par le fait qu'il faut fournir à la couche de détection de minima un signal balayé par une unité d'échantillonnage et un spectre de signal généré à partir de celui-ci par transformation de Fourier, laquelle couche de détection de minima détermine un minimum des amplitudes lissées des composants spectrales A (f, T), le lissage correspondant au calcul d'une moyenne temporelle sur m trames (ou « frames ») et la détection de minima s'étendant sur 1 trames, une trame correspondant à l'intervalle de temps sur lequel on doit effectuer une transformation de Fourier.
  5. Procédé selon l'une des revendications 1 à 4, caractérisé par le fait qu'un réseau neuronal à plusieurs couches génère une fonction de filtrage F (f, T) à partir d'un spectre de signal A (f, T), lequel doit être généré par transformation de Fourier sur une trame du signal d'entrée x(t) et le spectre A (f, T) doit être multiplié par la fonction de filtrage F (f, T) pour générer un spectre à bruit réduit B(f, T) à partir duquel, par application d'une transformation de Fourier inverse dans une unité de synthèse, doit être généré un signal vocal à bruit réduit y(t), t désignant le temps de traitement d'un échantillon des signaux x et/ou y.
  6. Procédé selon les revendications 1 à 5, caractérisé par le fait que les caractéristiques spectrales du signal vocal sont analysées dans la couche de diffusion dont les noeuds de calcul exécutent un couplage de composantes spectrales voisines dans le spectre à la manière d'une diffusion dans l'espace des fréquences, avec une constante de diffusion D > 0.
  7. Procédé selon les revendications 1 à 6, caractérisé par le fait que toutes les composantes spectrales de la fonction de filtrage F (f, T) à l'instant T sont multipliées par les amplitudes A (f, T) correspondantes.
  8. Procédé selon la revendication 7, caractérisé par le fait que l'atténuation du filtre pour les composantes de signal ayant des fréquences de modulation comprises entre 0,6 et 6 Hz est inférieure à 3 dB pour toutes les valeurs du paramètre de contrôle K, de sorte que ces composantes de signal passent le filtre, tandis que les composantes de fréquence ayant des fréquences de modulation situées en dehors de l'intervalle 0,6 à 6 Hz sont identifiées comme du bruit et atténuées plus fortement, en fonction du réglage du paramètre de contrôle K.
  9. Dispositif de réduction du bruit durant la transmission de parole, en particulier avec un procédé selon les revendications 1 à 8, caractérisé par un réseau neuronal avec
    une couche de détection de minima qui détecte des minima sur des spectres de signal précédents,
    une couche de réaction qui exécute une fonction de réaction non linéaire sur les minima détectés par la couche de détection de minima,
    une couche de diffusion qui exécute un lissage spectral sur les sorties de la couche de réaction avec seulement des couplages locaux de noeuds de calcul voisins dans la couche de diffusion,
    une couche d'intégration qui cumule dans un noeud de calcul la sortie de la couche de diffusion sans pondération,
    une fonction de filtrage F(f, T) pour le filtrage du bruit étant obtenue par couplage de noeuds de calcul des couches successives couche de détection de minima, couche de réaction, couche de diffusion et couche d'intégration, les composantes spectrales se différenciant par la fréquence f et correspondant à des noeuds de calcul individuels de chaque couche du réseau neuronal, à l'exception de la couche d'intégration, et chaque noeud de calcul de la couche de détection de minima déterminant une valeur M (f, T) pour la composante de fréquence f à l'instant T, M (f, T) étant obtenue par calcul de la moyenne temporelle des amplitudes A (f, T) sur un intervalle de temps qui correspond à m trames (ou « frames ») et la détection de minima s'étendant sur un intervalle de temps qui correspond à 1 trames, avec 1 > m.
  10. Dispositif selon la revendication 9, caractérisé par le fait que chaque noeud de calcul de la couche de réaction détermine à l'aide d'une fonction de réaction r[S(T-1)], obtenue à partir du signal intégré S(T-1) et d'un paramètre librement choisi K, lequel détermine le degré de réduction du bruit, à partir de A (f, T) et M (f, T) une composante du spectre relatif R (f, T) telle que R (f, T) = 1 - M(f, T)r[S(T-1)]K/A(f, T) avec la fonction de réaction r[S(T-1)].
  11. Dispositif selon les revendications 9 et 10, caractérisé par le fait que la plage de valeurs de la fonction de réaction est limitée sur un intervalle [r1, r2], R(f, T) devant être mis = 1 si R(f, T) > 1 et R(f, T) devant être mis = 0 si R(f, T) < 0.
  12. Dispositif selon les revendications 9 à 11, caractérisé par le fait que l'on met à la disposition des noeuds de calcul de la couche de réaction un signal intégré S(T-1) de la trame précédente (instant T-1) calculé dans la couche d'intégration et réinjecté dans la couche de réaction.
  13. Dispositif selon les revendications 9 à 12, caractérisé par le fait que pour les fréquences de modulation comprises entre 0,6 et 6 Hz, l'atténuation pour toutes les valeurs du paramètre de contrôle K est inférieure à 3 dB.
EP00250301A 1999-10-06 2000-09-08 Procédé et dispositif pour la réduction de bruit durant la transmission de parole Expired - Lifetime EP1091349B1 (fr)

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DE19948308A DE19948308C2 (de) 1999-10-06 1999-10-06 Verfahren und Vorrichtung zur Geräuschunterdrückung bei der Sprachübertragung
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Families Citing this family (38)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8606851B2 (en) 1995-06-06 2013-12-10 Wayport, Inc. Method and apparatus for geographic-based communications service
US5835061A (en) 1995-06-06 1998-11-10 Wayport, Inc. Method and apparatus for geographic-based communications service
EP1585112A1 (fr) 2004-03-30 2005-10-12 Dialog Semiconductor GmbH Suppression de bruit sans retard
DE102004031638A1 (de) * 2004-06-30 2006-01-26 Abb Patent Gmbh Verfahren zum Betrieb einer magnetisch induktiven Durchflussmesseinrichtung
DE102005039621A1 (de) 2005-08-19 2007-03-01 Micronas Gmbh Verfahren und Vorrichtung zur adaptiven Reduktion von Rausch- und Hintergrundsignalen in einem sprachverarbeitenden System
GB0703275D0 (en) * 2007-02-20 2007-03-28 Skype Ltd Method of estimating noise levels in a communication system
DE102007033484A1 (de) 2007-07-18 2009-01-22 Ruwisch, Dietmar, Dr. Hörgerät
EP2151822B8 (fr) * 2008-08-05 2018-10-24 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Appareil et procédé de traitement d'un signal audio pour amélioration de la parole utilisant une extraction de fonction
US20120245927A1 (en) * 2011-03-21 2012-09-27 On Semiconductor Trading Ltd. System and method for monaural audio processing based preserving speech information
US8239196B1 (en) * 2011-07-28 2012-08-07 Google Inc. System and method for multi-channel multi-feature speech/noise classification for noise suppression
EP2590165B1 (fr) 2011-11-07 2015-04-29 Dietmar Ruwisch Procédé et appareil pour générer un signal audio à bruit réduit
US9258653B2 (en) 2012-03-21 2016-02-09 Semiconductor Components Industries, Llc Method and system for parameter based adaptation of clock speeds to listening devices and audio applications
EP2786376A1 (fr) 2012-11-20 2014-10-08 Unify GmbH & Co. KG Procédé, dispositif et système de traitement de données audio
US9330677B2 (en) 2013-01-07 2016-05-03 Dietmar Ruwisch Method and apparatus for generating a noise reduced audio signal using a microphone array
AU2014374349B2 (en) * 2013-10-20 2017-11-23 Massachusetts Institute Of Technology Using correlation structure of speech dynamics to detect neurological changes
CN104036784B (zh) * 2014-06-06 2017-03-08 华为技术有限公司 一种回声消除方法及装置
US20160111107A1 (en) * 2014-10-21 2016-04-21 Mitsubishi Electric Research Laboratories, Inc. Method for Enhancing Noisy Speech using Features from an Automatic Speech Recognition System
EP3301675B1 (fr) 2016-09-28 2019-08-21 Panasonic Intellectual Property Corporation of America Dispositif de prédiction de paramètres et procédé de prédiction de paramètres pour traitement de signal acoustique
WO2018204917A1 (fr) 2017-05-05 2018-11-08 Ball Aerospace & Technologies Corp. Détection spectrale et attribution à l'aide d'un apprentissage automatique profond
CN109427340A (zh) * 2017-08-22 2019-03-05 杭州海康威视数字技术股份有限公司 一种语音增强方法、装置及电子设备
US10283140B1 (en) * 2018-01-12 2019-05-07 Alibaba Group Holding Limited Enhancing audio signals using sub-band deep neural networks
US11182672B1 (en) 2018-10-09 2021-11-23 Ball Aerospace & Technologies Corp. Optimized focal-plane electronics using vector-enhanced deep learning
US10879946B1 (en) * 2018-10-30 2020-12-29 Ball Aerospace & Technologies Corp. Weak signal processing systems and methods
WO2020117530A1 (fr) 2018-12-03 2020-06-11 Ball Aerospace & Technologies Corp. Suiveur stellaire permettant la détection et le suivi multimode de cibles sombres
US11851217B1 (en) 2019-01-23 2023-12-26 Ball Aerospace & Technologies Corp. Star tracker using vector-based deep learning for enhanced performance
US11412124B1 (en) 2019-03-01 2022-08-09 Ball Aerospace & Technologies Corp. Microsequencer for reconfigurable focal plane control
EP3726529A1 (fr) * 2019-04-16 2020-10-21 Fraunhofer Gesellschaft zur Förderung der Angewand Procédé et appareil permettant de déterminer un filtre profond
US11488024B1 (en) 2019-05-29 2022-11-01 Ball Aerospace & Technologies Corp. Methods and systems for implementing deep reinforcement module networks for autonomous systems control
US11303348B1 (en) 2019-05-29 2022-04-12 Ball Aerospace & Technologies Corp. Systems and methods for enhancing communication network performance using vector based deep learning
EP3764664A1 (fr) 2019-07-10 2021-01-13 Analog Devices International Unlimited Company Procédés et systèmes de traitement de signal pour la formation de faisceau à compensation de tolérance de microphone
EP3764359A1 (fr) 2019-07-10 2021-01-13 Analog Devices International Unlimited Company Procédés et systèmes de traitement de signal pour formation de faisceaux multifocaux
EP3764358B1 (fr) 2019-07-10 2024-05-22 Analog Devices International Unlimited Company Procédés et systèmes de traitement de signaux pour la formation de faisceaux avec protection contre les effets du vent
EP3764360B1 (fr) 2019-07-10 2024-05-01 Analog Devices International Unlimited Company Procédés et systèmes de traitement de signaux pour la formation de faisceau avec amélioration du rapport signal-bruit
EP3764660B1 (fr) 2019-07-10 2023-08-30 Analog Devices International Unlimited Company Procédés et systèmes de traitement de signaux pour la formation adaptative de faisceau
US11828598B1 (en) 2019-08-28 2023-11-28 Ball Aerospace & Technologies Corp. Systems and methods for the efficient detection and tracking of objects from a moving platform
IT201900024454A1 (it) * 2019-12-18 2021-06-18 Storti Gianampellio Apparecchio audio con basso consumo per ambienti rumorosi
CN114944154B (zh) * 2022-07-26 2022-11-15 深圳市长丰影像器材有限公司 音频调整方法、装置、设备及存储介质
US20240112690A1 (en) * 2022-09-26 2024-04-04 Cerence Operating Company Switchable Noise Reduction Profiles

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3610831A (en) * 1969-05-26 1971-10-05 Listening Inc Speech recognition apparatus
US5822742A (en) * 1989-05-17 1998-10-13 The United States Of America As Represented By The Secretary Of Health & Human Services Dynamically stable associative learning neural network system
US5581662A (en) * 1989-12-29 1996-12-03 Ricoh Company, Ltd. Signal processing apparatus including plural aggregates
JPH0566795A (ja) * 1991-09-06 1993-03-19 Gijutsu Kenkyu Kumiai Iryo Fukushi Kiki Kenkyusho 雑音抑圧装置とその調整装置
US5377302A (en) * 1992-09-01 1994-12-27 Monowave Corporation L.P. System for recognizing speech
DE4309985A1 (de) * 1993-03-29 1994-10-06 Sel Alcatel Ag Geräuschreduktion zur Spracherkennung
IT1270919B (it) * 1993-05-05 1997-05-16 Cselt Centro Studi Lab Telecom Sistema per il riconoscimento di parole isolate indipendente dal parlatore mediante reti neurali
US5649065A (en) * 1993-05-28 1997-07-15 Maryland Technology Corporation Optimal filtering by neural networks with range extenders and/or reducers
DE69428119T2 (de) * 1993-07-07 2002-03-21 Picturetel Corp Verringerung des hintergrundrauschens zur sprachverbesserung
US5878389A (en) * 1995-06-28 1999-03-02 Oregon Graduate Institute Of Science & Technology Method and system for generating an estimated clean speech signal from a noisy speech signal
US5960391A (en) * 1995-12-13 1999-09-28 Denso Corporation Signal extraction system, system and method for speech restoration, learning method for neural network model, constructing method of neural network model, and signal processing system
US5717833A (en) * 1996-07-05 1998-02-10 National Semiconductor Corporation System and method for designing fixed weight analog neural networks

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EP1091349A3 (fr) 2002-01-02
EP1091349A2 (fr) 2001-04-11
US6820053B1 (en) 2004-11-16
DE19948308C2 (de) 2002-05-08
CA2319995A1 (fr) 2001-04-06
CA2319995C (fr) 2005-04-26
TW482993B (en) 2002-04-11
DE19948308A1 (de) 2001-04-19
ATE289110T1 (de) 2005-02-15
DE50009461D1 (de) 2005-03-17

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