EP1091349A2 - 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
EP1091349A2
EP1091349A2 EP00250301A EP00250301A EP1091349A2 EP 1091349 A2 EP1091349 A2 EP 1091349A2 EP 00250301 A EP00250301 A EP 00250301A EP 00250301 A EP00250301 A EP 00250301A EP 1091349 A2 EP1091349 A2 EP 1091349A2
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European Patent Office
Prior art keywords
layer
spectrum
noise
filter
signal
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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
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EP00250301A
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German (de)
English (en)
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EP1091349A3 (fr
EP1091349B1 (fr
Inventor
Dietmar Dr. Ruwisch
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RUWISCH, DIETMAR, DR.
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Cortologic 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
    • 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 and a device for noise suppression during voice transmission through a multi-layered, self-organizing, feedback neural network.
  • the Wiener Komolgorov filter is derived from the optimal filter theory. (S.V. Vaseghi, Advanced Signal Processing and Digital Noise Reduction, "John Wiley and Teubner publishing house, 1996). This procedure is based on the Minimize the mean square error between the actual and expected speech signal. This filter concept requires a considerable amount Computing effort. It is also like most known ones Procedure a stationary interference signal theoretical requirement.
  • the Kalman filter is based on a similar filter principle (E. Wan and A. Nelson, Removal of noise from speech using the Dual Extended Kalman Filter algorithm, Proceedings of the IEEE International Conference on Acoustics and Signal Processing (ICASSP'98), Seattle 1998).
  • This filter concept has a disadvantage the long training time it takes to complete the Determine filter parameters.
  • LPC requires the complex one Calculation of correlation matrices in order to use a linear prediction method filter coefficients calculate as from T. Arai, H. Hermansky, M. Paveland, C. Avendano, Intelligibility of Speech with Filtered Time Trajectories of LPC Cepstrum, The Journal of the Acoustical Society of Maerica, Vol. 100, No. 4, Pt. 2, p. 2756, 1996.
  • the object of the present invention is a method to create that with little computation Speech signal based on its temporal and spectral properties recognized and freed from noise can.
  • This task is solved in that a mini detection layer, a reaction layer, a diffusion layer and an integration layer a filter function Determine F (f, T) for noise filtering.
  • a network designed in this way recognizes a voice signal in its temporal and spectral properties and frees it from noise. Compared to In known methods, the computation effort required is low.
  • the process is characterized by a special short adaptation time, within which the system adjusts to the type of noise.
  • the signal delay when processing the signal very short, so that the filter in real time for telecommunications is operational.
  • FIG. 10 An overall system is shown schematically and by way of example in FIG shown for language filtering. This exists from a sampling unit 10, which is the noisy one Voice signal sampled in time t and discretized and thus generates samples x (t) that in time T can be combined into frames from n samples.
  • the Fourier transform is used to transform each frame Spectrum A (f, T) determined at time T and a filter unit 11 fed with the help of a neural Network, as shown in Figure 2, a Filter function F (f, T) calculated with which the spectrum A (f, T) of the signal is multiplied by the noise-free Generate spectrum B (f, T). Subsequently becomes the signal of a synthesis unit filtered in this way (12) passed by means of inverse Fourier transformation the noise-free from the filtered spectrum B (f, T) Speech signal y (t) synthesized.
  • FIG. 2 shows a mini detection layer, a reaction layer, a diffusion layer and one Neural network containing integration layer, which is particularly the subject of the invention and which the spectrum A (f, T) of the input signal supplied becomes, from which the filter function F (f, T) is calculated becomes.
  • Each of the modes of the spectrum that stands out distinguish the frequency f corresponds to one single neuron per layer of the network except the integration layer.
  • the individual layers are specified in more detail in the following figures.
  • M (f, T) determines.
  • M (f, T) is in fashion with frequency f the minimum of the averaged over m frames Amplitude A (f, T) within an interval of Time T, which corresponds to the length of 1 frame.
  • Figure 4 shows a neuron of the reaction layer, which with the help of a reaction function r [S (T-1)] from the integral signal S (T-1) as detailed in Figure 6 is shown, and a freely selectable parameter K, which determines the level of noise cancellation the relative spectrum R (f, T) is determined from A (f, T) and M (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 local coupling corresponding to the diffusion between the fashions.
  • the diffusion constant D determines the strength of the resulting Smoothing over the frequencies f at a fixed time T.
  • the diffusion layer determines from the relative signal R (f, T) the actual filter function F (f, T) with which the spectrum A (f, T) is multiplied by noise to eliminate.
  • Speech of sounds based on spectral properties distinguished.
  • Figure 6 shows this in the chosen embodiment of the invention only neuron of the integration layer that the Filter function F (f, T) with fixed time T over the frequencies f integrated and the integral signal thus obtained S (T) feeds back into the reaction layer, as Figure 2 shows.
  • This global coupling ensures that when there is a high noise level, the filtering is strong while noiseless speech is transmitted unadulterated.
  • FIG. 7 shows exemplary details of the filter properties of the invention for various settings of the control parameter K.
  • the picture shows the Attenuation of amplitude modulated white noise in Dependence of the modulation frequency. At modulation frequencies the damping is between 0.6 Hz and 6 Hz less than 3 dB. This interval corresponds to the typical modulation of human language.
  • a Speech signal that is affected by any background noise be sampled in a sampling unit 10 and digitized, as shown in FIG. 1.
  • samples x (t) are obtained in time t.
  • samples x (t) are combined into a frame, of which at time T using Fourier transform a spectrum A (f, T) is calculated.
  • the modes of the spectrum differ in their Frequency f.
  • the Spectrum A (f, T) generates 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 through inverse Fourier transformation the noise-free Speech signal y (t) is generated. This can after digital-analog conversion in a speaker be made audible.
  • the filter function F (f, T) is performed by a neural Network that creates a mini detection layer, a reaction layer, a diffusion layer and one Integration layer contains, as Figure 2 shows.
  • the Spectrum A (f, T) generated by the sampling unit 10 is first fed to the mini detection layer, as shown in Figure 3.
  • a single neuron of this layer works independently from the other neurons of the mini-detection layer a single fashion by the frequency f is marked. The neuron averages for this fashion the amplitudes A (f, T) in time T over m frames. Of the neuron then determines these averaged amplitudes over a period in T that is the length of 1 frames the minimum for his fashion. To this In this way, the neurons of the mini detection layer generate the signal M (f, T), which is then fed to the reaction layer becomes.
  • Each neuron of the reaction layer as shown in FIG. 4 shows, edited a single mode of frequency f, independent of the other neurons in this layer.
  • all neurons also have an externally adjustable one Paramter K fed, the size of the degree of Noise suppression of the entire filter also determines the integral signal S (T-1) from the previous frame (time T-1) in the integration layer, as shown in FIG. 6 has been.
  • This signal is the argument of a non-linear reaction function r, with the help of the neurons of the reaction layer the relative spectrum R (f, T) at the time Calculate T.
  • the value range of the reaction function is on an interval [r1, r2] restricted.
  • the range of values of the resulting relative spectrum R (f, T) is limited to the interval [0, 1].
  • the temporal behavior is in the reaction layer of the speech signal to distinguish 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 local mode coupling according to Art perform a diffusion in the frequency domain.
  • This integral signal is fed back into the reaction layer.
  • This global coupling means that the strength of the signal manipulation in the filter from the interference level is dependent. Voice signals with low noise levels pass the filter practically unaffected, while at high noise levels a strong filter effect takes effect. This makes the Invention of classic bandpass filters, their influence on the signal only from the selected, fixed predetermined Parameters.
  • the item has the invention no frequency response in the conventional Senses.
  • the filter properties of the test signal influence When measuring with a tunable sinusoidal test signal would already change the modulation speed the filter properties of the test signal influence.
  • a suitable method for analyzing the properties the filter uses an amplitude-modulated noise signal, to the depending on the modulation frequency
  • This "modulation course" is shown in FIG. 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)
  • Reduction Or Emphasis Of Bandwidth Of Signals (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Noise Elimination (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)
  • Telephone Function (AREA)
  • Transmission Systems Not Characterized By The Medium Used For Transmission (AREA)
EP00250301A 1999-10-06 2000-09-08 Procédé et dispositif pour la réduction de bruit durant la transmission de parole Expired - Lifetime EP1091349B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19948308 1999-10-06
DE19948308A DE19948308C2 (de) 1999-10-06 1999-10-06 Verfahren und Vorrichtung zur Geräuschunterdrückung bei der Sprachübertragung

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EP1091349A2 true EP1091349A2 (fr) 2001-04-11
EP1091349A3 EP1091349A3 (fr) 2002-01-02
EP1091349B1 EP1091349B1 (fr) 2005-02-09

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US (1) US6820053B1 (fr)
EP (1) EP1091349B1 (fr)
AT (1) ATE289110T1 (fr)
CA (1) CA2319995C (fr)
DE (2) DE19948308C2 (fr)
TW (1) TW482993B (fr)

Cited By (4)

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EP1585112A1 (fr) * 2004-03-30 2005-10-12 Dialog Semiconductor GmbH Suppression de bruit sans retard
EP2151822A1 (fr) * 2008-08-05 2010-02-10 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Appareil et procédé de traitement et signal audio pour amélioration de la parole utilisant une extraction de fonction
CN104036784A (zh) * 2014-06-06 2014-09-10 华为技术有限公司 一种回声消除方法及装置
CN114944154A (zh) * 2022-07-26 2022-08-26 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

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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
CN104160443B (zh) 2012-11-20 2016-11-16 统一有限责任两合公司 用于音频数据处理的方法、设备和系统
US9330677B2 (en) 2013-01-07 2016-05-03 Dietmar Ruwisch Method and apparatus for generating a noise reduced audio signal using a microphone array
CA2928005C (fr) * 2013-10-20 2023-09-12 Massachusetts Institute Of Technology Utilisation d'une structure de correlation d'une dynamique de parole pour detecter des changements neurologiques
US9881631B2 (en) * 2014-10-21 2018-01-30 Mitsubishi Electric Research Laboratories, Inc. Method for enhancing audio signal using phase information
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
US11190944B2 (en) 2017-05-05 2021-11-30 Ball Aerospace & Technologies Corp. Spectral sensing and allocation using deep machine learning
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
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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
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
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
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
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1585112A1 (fr) * 2004-03-30 2005-10-12 Dialog Semiconductor GmbH Suppression de bruit sans retard
EP2151822A1 (fr) * 2008-08-05 2010-02-10 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Appareil et procédé de traitement et signal audio pour amélioration de la parole utilisant une extraction de fonction
WO2010015371A1 (fr) * 2008-08-05 2010-02-11 Fraunhofer-Gesellschaft Zur Förderung Der Angewandten Forschung E . V . Appareil et procédé de traitement d'un signal audio pour une amélioration vocale à l'aide d'une extraction de caractéristique
CN102124518A (zh) * 2008-08-05 2011-07-13 弗朗霍夫应用科学研究促进协会 采用特征提取处理音频信号用于语音增强的方法和装置
AU2009278263B2 (en) * 2008-08-05 2012-09-27 Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E . V . Apparatus and method for processing an audio signal for speech enhancement using a feature extraction
CN102124518B (zh) * 2008-08-05 2013-11-06 弗朗霍夫应用科学研究促进协会 采用特征提取处理音频信号用于语音增强的方法和装置
US9064498B2 (en) 2008-08-05 2015-06-23 Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. Apparatus and method for processing an audio signal for speech enhancement using a feature extraction
CN104036784A (zh) * 2014-06-06 2014-09-10 华为技术有限公司 一种回声消除方法及装置
CN114944154A (zh) * 2022-07-26 2022-08-26 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

Also Published As

Publication number Publication date
EP1091349A3 (fr) 2002-01-02
US6820053B1 (en) 2004-11-16
DE19948308A1 (de) 2001-04-19
DE19948308C2 (de) 2002-05-08
TW482993B (en) 2002-04-11
CA2319995C (fr) 2005-04-26
EP1091349B1 (fr) 2005-02-09
ATE289110T1 (de) 2005-02-15
DE50009461D1 (de) 2005-03-17
CA2319995A1 (fr) 2001-04-06

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