WO2000049602A1 - Systeme, procede et appareil de suppression du bruit - Google Patents

Systeme, procede et appareil de suppression du bruit Download PDF

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Publication number
WO2000049602A1
WO2000049602A1 PCT/US2000/003538 US0003538W WO0049602A1 WO 2000049602 A1 WO2000049602 A1 WO 2000049602A1 US 0003538 W US0003538 W US 0003538W WO 0049602 A1 WO0049602 A1 WO 0049602A1
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WIPO (PCT)
Prior art keywords
noise
minimum value
audio signal
threshold
frequency
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Application number
PCT/US2000/003538
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English (en)
Inventor
Joseph Marash
Baruch Berdugo
Original Assignee
Andrea Electronics Corporation
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Priority claimed from US09/252,874 external-priority patent/US6363345B1/en
Application filed by Andrea Electronics Corporation filed Critical Andrea Electronics Corporation
Priority to CA002358710A priority Critical patent/CA2358710A1/fr
Priority to JP2000600263A priority patent/JP2002537586A/ja
Priority to IL14398900A priority patent/IL143989A0/xx
Priority to EP00908595A priority patent/EP1157376A1/fr
Publication of WO2000049602A1 publication Critical patent/WO2000049602A1/fr

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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
    • G10L25/78—Detection of presence or absence of voice signals
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering

Definitions

  • the present invention relates to noise cancellation and reduction and. more specifically, ⁇ noise cancellation and reduction using spectral subtraction. BACKGROUND OF THE INVENTION.
  • processing algorithms may include dictation, voice activation, voice compression and othei systems. In such systems, it is desired to reduce the noise and improve the signal to noise ratio (S/N ratio) without effecting the speech and its characteristics.
  • S/N ratio signal to noise ratio
  • Near field noise canceling microphones provide a satisfactory solution bu' require that the microphone in the proximity of the voice source (e.g., mouth). n many cases, this is achieved by mounting the mic ophone on - ⁇ boom of a headset many cases, this is achieved by mounting the microphone on a boom of a headset which situates the microphone at the end of a boom proximate the mouth of the wearer.
  • the headset has proven to be either uncomfortable to wear or too restricting for operation in, for example, an automobile.
  • Microphone array technology in general, and adaptive beamforming arrays in particular handle severe directional noises in the most efficient way. These systems map the noise field and create nulls towards the noise sources. The number of nulls is limited by the number of microphone elements and processing power.
  • Such arrays have the benefit of hands-free operation without the necessity of a headset.
  • the spectral subtraction technique provides a solution to further reduce the noise by estimating the noise magnitude spectrum of the polluted signal.
  • the technique estimates the magnitude spectral level of the noise by measuring it during non-speech time intervals detected by a voice switch, and then subtracting the noise magnitude spectrum from the signal.
  • This method described in detail in Suppression of Acoustic Noise in Speech Using Spectral Subtraction, (Steven F Boll, IEEE ASSP- 27 NO.2 April, 1979), achieves good results for stationary diffused noises that are not correlated with the speech signal.
  • the spectral subtraction method creates artifacts, sometimes described as musical noise, that may reduce the performance of the speech algorithm (such as vocoders or voice activation) if the spectral subtraction is uncontrolled.
  • the spectral subtraction method assumes erroneously that the voice switch accurately detects the presence of speech and locates the non-speech time intervals. This assumption is reasonable for off-line systems but difficult to achieve or obtain in real time systems.
  • the noise magnitude spectrum is estimated by performing an FFT of 256 points of the non-speech time intervals and computing the energy of each frequency bin. The FFT is performed after the time domain signal is multiplied by a shading window (Harming or other) with an overlap of 50%. The energy of each frequency bin is averaged with neighboring FFT time frames. The number of frames is not determined but depends on the stability of the noise. For a stationary noise, it is preferred that many frames are averaged to obtain better noise estimation. For a non-stationary noise, a long averaging may be harmful.
  • the input signal is multiplied by a shading window (Harming or other), an FFT is performed (256 points or other) with an overlap of 50% and the magnitude of each bin is averaged over 2-3 FFT frames.
  • the noise magnitude spectrum is then subtracted from the signal magnitude. If the result is negative, the value is replaced by a zero (Half Wave Rectification). It is recommended, however, to further reduce the residual noise present during non-speech intervals by replacing low values with a minimum value (or zero) or by attenuating the residual noise by 3OdB.
  • the resulting output is the noise free magnitude spectrum.
  • the spectral complex data is reconstructed by applying the phase information of the relevant bin of the signal's FFT with the noise free magnitude.
  • An IFFT process is then performed on the complex data to obtain the noise free time domain data. The time domain results are overlapped and summed with the previous frame's results to compensate for the overlap process of the FFT.
  • a voice switch detects the presence of the speech by measuring the energy level and comparing it to a threshold. If the threshold is too high, there is a risk that some voice time intervals might be regarded as a non-speech time interval and the system will regard voice information as noise. The result is voice distortion, especially in poor signal to noise ratio cases. If, on the other hand, the threshold is too low, there is a risk that the non-speech intervals will be too short especially in poor signal to noise ratio cases and in cases where the voice is continuous with little intermission
  • the present invention provides a system that correctly determines the non-speech segments of the audio signal thereby preventing erroneous processing of the noise canceling signal during the speech segments
  • the present invention obviates the need for a voice switch by precisely determining the non-speech segments using a separate threshold detector for each frequency bm
  • the threshold detector precisely detects the positions of the noise elements, even within continuous speech segments, by determining whether frequency spectrum elements, or bins, of the input signal are within a threshold set according to a minimum value of the frequency spectrum elements over a preset period of time. More precisely, current and future minimum values of the frequency spectrum elements.
  • the energy of the noise elements is determined by a separate threshold determination without examination of the overall signal energy thereby providing good and stable estimation of the noise.
  • the system preferably sets the threshold continuously and resets the threshold within a predetermined period of time of, for example, five seconds.
  • an estimate of the magnitude of the input audio signal using a multiplying combination of the real and imaginary parts of the input in accordance with, for example, the higher and the lower values of the real and imaginary parts of the signal.
  • a two- dimensional (2D) smoothing process is applied to the signal estimation.
  • a two-step smoothing function using first neighboring frequency bins in each time frame then applying an exponential time average effecting an average over time for each frequency bin produces excellent results.
  • the present invention applies a filter multiplication to effect the subtraction.
  • the filter function a Weiner filter function for example, or an approximation of the Weiner filter is multiplied by the complex data of the frequency domain audio signal.
  • the filter function may effect a full-wave rectification, or a half-wave rectification for otherwise negative results of the subtraction process or simple subtraction. It will be appreciated that, since the noise elements are determined within continuous speech segments, the noise estimation is accurate and it may be canceled from the audio signal continuously providing excellent noise cancellation characteristics.
  • the present invention also provides a residual noise reduction process for reducing the residual noise remaining after noise cancellation.
  • the residual noise is reduced by zeroing the non-speech segments, e.g., within the continuous speech, or decaying the non-speech segments.
  • a voice switch may be used or another threshold detector which detects the non-speech segments in the time-domain.
  • the present invention is applicable with various noise canceling systems including, but not limited to, those systems described in the U.S. patent applications incorporated herein by reference.
  • the present invention for example, is applicable with the adaptive beamforming array.
  • the present invention may be embodied as a computer program for driving a computer processor either installed as application software or as hardware.
  • Fig. 1 illustrates the present invention
  • Fig. 2 illustrates the noise processing of the present invention
  • Fig. 3 illustrates the noise estimation processing of the present invention
  • Fig. 4 illustrates the subtraction processing of the present invention
  • Fig. 5 illustrates the residual noise processing of the present invention
  • Fig. 5 A illustrates a variant of the residual noise processing of the present invention
  • Fig. 6 illustrates a flow diagram of the present invention
  • Fig. 7 illustrates a flow diagram of the present invention
  • Fig. 8 illustrates a flow diagram of the present invention.
  • Fig. 9 illustrates a flow diagram of the present invention.
  • the present invention in one embodiment, practicable as a spectral subtraction system, method and apparatus for canceling and/or reducing noise arising from electrical or electromagnetic noise sources such as external electromagnetic noise sources such as power sources including, power supplies such as an AC source, an AC to DC power converter such as used by a computer, particularly a lap-top computer.
  • power supplies such as an AC source, an AC to DC power converter such as used by a computer, particularly a lap-top computer.
  • the power supply of a computer such as a lap-top device creates an interference noise on or in relation to the Universal Serial Bus (USB) line, port or signal thereon.
  • USB Universal Serial Bus
  • the power source on the power conversion creates an interference signal (herein referred to as "isotropic diffused stationary noise” or “isotropic noise”) which is transposed through, for example, electromagnetic coupling to the USB signal line which interferes with the signals thereon.
  • This noise is audible when reproduced by a transducer, for example, as a buzzing sound.
  • USB was previously thought to avoid the audio noise present from such sources which manifests on such devices as sound cards. Since
  • USB is rapidly becoming the standard for speech and voice communications applications, for example, received from audio signal peripherals including signals received over the internet or other remote-transmission medium, it is a significant feat to eliminate this isotropic noise and, indeed, would have the same impact on the market as when DolbyTM was invented.
  • the present invention as described herein was discovered to eliminate the "dirty noise" arising from the power source or power converter and manifesting on the USB signal line.
  • One skilled in the art will appreciate how the spectral subtraction system, method and apparatus described herein is embodied as any of the well-known computer software and/or hardware applications on a computer including, for example, a device driver or dynamic link library as particularly set forth in related application U.S. Patent Application Serial No. 60/126,567.
  • the present invention includes filters selectable by pull down menus for filtering out the isotropic noise.
  • the preferred operating range of the present invention is scalable either automatically at the control of a computer processor or manually by the user by way of, for example, potentiometer or clickable object presented by the tabbed pull-down tabbed, between 8dB to 14dB since this appeared to provide optimal performance although it is within the present invention to provide other dB ranges.
  • potentiometer or clickable object presented by the tabbed pull-down tabbed between 8dB to 14dB since this appeared to provide optimal performance although it is within the present invention to provide other dB ranges.
  • noise reduction is above 14dB, speech is attacked and there can be degradation of speech recognition.
  • the present invention is applicable to inherent system noise or noise induced by a system, as well as to accoustical noise; and the invention reduces or eliminates inherent system noise induced by a system (e.g., noise from a power source or power converter), as well as accoustical noise.
  • Figure 1 illustrates an embodiment of the present invention 100.
  • the system receives a digital audio signal at input 102 sampled at a frequency which is at least twice the bandwidth of the audio signal.
  • the signal is derived from a microphone signal that has been processed through an analog front end, A/D converter and a decimation filter to obtain the required sampling frequency.
  • the input is taken from the output of a beamformer or even an adaptive beamformer.
  • the signal has been processed to eliminate noises arriving from directions other than the desired one leaving mainly noises originated from the same direction of the desired one.
  • the input signal can be obtained from a sound board when the processing is implemented on a
  • the input samples are stored in a temporary buffer 104 of 256 points. When the buffer is full, the new 256 points are combined in a combiner 106 with the previous 256 points to provide 512 input points.
  • the 512 input points are multiplied by multiplier 108 with a shading window with the length of 512 points.
  • the shading window contains coefficients that are multiplied with the input data accordingly.
  • the shading window can be Hanning or other and it serves two goals: the first is to smooth the transients between two processed blocks (together with the overlap process); the second is to reduce the side lobes in the frequency domain and hence prevent the masking of low energy tonals by high energy side lobes.
  • the shaded results are converted to the frequency domain through an FFT (Fast Fourier Transform) processor 110.
  • FFT Fast Fourier Transform
  • the FFT output is a complex vector of 256 significant points (the other 256 points are an anti-symmetric replica of the first 256 points).
  • the points are processed in the noise processing block 112(200) which includes the noise magnitude estimation for each frequency bin - the subtraction process that estimates the noise- free complex value for each frequency bin and the residual noise reduction process.
  • An IFFT (Inverse Fast Fourier Transform) processor 114 performs the Inverse Fourier Transform on the complex noise free data to provide 512 time domain points.
  • the first 256 time domain points are summed by the summer 116 with the previous last 256 data points to compensate for the input overlap and shading process and output at output terminal 118.
  • the remaining 256 points are saved for the next iteration.
  • each frequency bin (n) 202 magnitude is estimated.
  • the straight forward approach is to estimate the magnitude by calculating:
  • each bin is replaced with the average of its value and the two neighboring bins' value (of the same time frame) by a first averager 206.
  • the smoothed value of each smoothed bin is further smoothed by a second averager 208 using a time exponential average with a time constant of 0.7 (which is the equivalent of averaging over 3 time frames).
  • the 2D-smoofhed value is then used by two processes - the noise estimation process by noise estimation processor 212(300) and the subtraction process by subtractor 210.
  • the noise estimation process estimates the noise at each frequency bin and the result is used by the noise subtraction process.
  • the output of the noise subtraction is fed into a residual noise reduction processor 216 to further reduce the noise.
  • the time domain signal is also used by the residual noise process 216 to determine the speech free segments.
  • the noise free signal is moved to the IFFT process to obtain the time domain output 218.
  • Figure 3 is a detailed description of the noise estimation processor 300(212).
  • the noise should be estimated by taking a long time average of the signal magnitude (Y) of non-speech time intervals. This requires that a voice switch be used to detect the speech/non-speech intervals.
  • Y signal magnitude
  • a less sensitive switch may dramatically reduce the length of the noise time intervals (especially in continuous speech cases) and defect the validity of the noise estimation.
  • a separate adaptive threshold is implemented for each frequency bin 302. This allows the location of noise elements for each bin separately without the examination of the overall signal energy.
  • the logic behind this method is that, for each syllable, the energy may appear at different frequency bands. At the same time, other frequency bands may contain noise elements. It is therefore possible to apply a non-sensitive threshold for the noise and yet locate many non- speech data points for each bin, even within a continuous speech case.
  • the advantage of this method is that it allows the collection of many noise segments for a good and stable estimation of the noise, even within continuous speech segments.
  • a future minimum value is initiated every 5 seconds at 304 with the value of the current magnitude (Y(n)) and replaced with a smaller minimal value over the next 5 seconds through the following process.
  • the future minimum value of each bin is compared with the current magnitude value of the signal. If the current magnitude is smaller than the future minimum, the future minimum is replaced with the magnitude which becomes the new future minimum.
  • a current minimum value is calculated at 306. The current minimum is initiated every 5 seconds with the value of the future minimum that was determined over the previous 5 seconds and follows the minimum value of the signal for the next 5 seconds by comparing its value with the current magnitude value.
  • the current minimum value is used by the subtraction process, while the future minimum is used for the initiation and refreshing of the current minimum.
  • the noise estimation mechanism of the present invention ensures a tight and quick estimation of the noise value, with limited memory of the process (5 seconds), while preventing a too high an estimation of the noise.
  • Each bin's magnitude (Y(n)) is compared with four times the current minimum value of that bin by comparator 308 - which serves as the adaptive threshold for that bin. If the magnitude is within the range (hence below the threshold), it is allowed as noise and used by an exponential averaging unit 310 that determines the level of the noise 312 of that frequency. If the magnitude is above the threshold it is rejected for the noise estimation.
  • the time constant for the exponential averaging is typically 0.95 which may be interpreted as taking the average of the last
  • the threshold of 4*minimum value may be changed for some applications.
  • Figure 4 is a detailed description of the subtraction processor 400(210).
  • the value of the estimated bin noise magnitude is subtracted from the current bin magnitude.
  • the phase of the current bin is calculated and used in conjunction with the result of the subtraction to obtain the Real and
  • H(n) ⁇ ⁇ Y ( n ) ⁇ - ⁇ N ( n ) ⁇ ⁇ ⁇ Y(n) ⁇
  • E is the noise free complex value.
  • the subtraction may result in a negative value of magnitude.
  • This value can be either replaced with zero (half-wave rectification) or replaced with a positive value equal to the negative one (full-wave rectification).
  • the filter approach results in the full-wave rectification directly.
  • the full wave rectification provides a little less noise reduction but introduces much less artifacts to the signal. It will be appreciated that this filter can be modified to effect a half-wave rectification by taking the non-absolute value of the numerator and replacing negative values with zeros. Note also that the values of Y in the figures are the smoothed values of
  • Figure 5 illustrates the residual noise reduction processor 500(216).
  • the residual noise is defined as the remaining noise during non-speech intervals.
  • the noise in these intervals is first reduced by the subtraction process which does not differentiate between speech and non-speech time intervals.
  • the remaining residual noise can be reduced further by using a voice switch 502 and either multiplying the residual noise by a decaying factor or replacing it with zeros. Another alternative to the zeroing is replacing the residual noise with a minimum value of noise at 504.
  • FIG. 5 A Yet another approach, which avoids the voice switch, is illustrated in Figure 5 A.
  • the residual noise reduction processor 506 applies a similar threshold used by the noise estimator at 508 on the noise free output bin and replaces or decays the result when it is lower than the threshold at 510.
  • the result of the residual noise processing of the present invention is a quieter sound in the non-speech intervals.
  • the appearance of artifacts such as a pumping noise when the noise level is switched between the speech interval and the non-speech interval may occur in some applications.
  • the spectral subtraction technique of the present invention can be utilized in conjunction with the array techniques, close talk microphone technique or as a stand alone system.
  • the spectral subtraction of the present invention can be implemented on an embedded hardware (DSP) as a stand alone system, as part of other embedded algorithms such as adaptive beamforming, or as a software application running on a PC using data obtained from a sound port.
  • DSP embedded hardware
  • the present invention may be implemented as a software application.
  • the input samples are read.
  • the read samples are stored in a buffer. If 256 new points are accumulated in step 604, program control advances to step 606 - otherwise control returns to step 600 where additional samples are read.
  • the last 512 points are moved to the processing buffer in step 606.
  • the 256 new samples stored are combined with the previous 256 points in step 608 to obtain the 512 points.
  • a Fourier Transform is performed on the 512 points. Of course, another transform may be employed to obtain the spectral noise signal.
  • the 256 significant complex points resulting from the transformation are stored in the buffer.
  • the second 256 points are a conjugate replica of the first 256 points and are redundant for real inputs.
  • the stored data in step 614 includes the 256 real points and the 256 imaginary points.
  • step 700 the noise processing is performed wherein the magnitude of the signal is estimated in step 700.
  • the straight forward approach may be employed but, as discussed with reference to Figure 2, the straight forward approach requires extraneous processing time and complexity.
  • step 702 the stored complex points are read from the buffer and calculated using the estimation equation shown in step 700. The result is stored in step 704.
  • a 2-dimensional (2D) smoothing process is effected in steps 706 and 708 wherein, in step 706, the estimate at each point is averaged with the estimates of adjacent points and, in step 708, the estimate is averaged using an exponential average having the effect of averaging the estimate at each point over, for example, 3 time samples of each bin.
  • the smoothed estimate is employed to determine the future minimum value and the current minimum value. If the smoothed estimate is less than the calculated future minimum value as determined in step 710, the future minimum value is replaced with the smoothed estimate and stored in step 714.
  • step 712 determines whether the smoothed estimate is less than the current minimum value. If it is determined at step 712 that the smoothed estimate is less than the current minimum value, then the current minimum is replaced with the smoothed estimate value and stored in step 720.
  • the future and current minimum values are calculated continuously and initiated periodically, for example, every 5 seconds as determined in step 724 and control is advanced to steps 722 and 726 wherein the new future and current minimum are calculated. Afterwards, control advances to Figure 8 as indicated by the circumscribed letter B where the subtraction and residual noise reduction are effected.
  • step 800 it is determined whether the samples are less than a threshold amount in step 800.
  • step 804 where the samples are within the threshold, the samples undergo an exponential averaging and stored in the buffer at step 802. Otherwise, control advances directly to step 808.
  • the filter coefficients are determined from the signal samples retrieved in step 806 the samples retrieved from step 810 is determined from the signal samples retrieved in step 806 and the estimated samples retrieved from step 810.
  • step 814 the filter transform is multiplied by the samples retrieved from steps 816 and stored in step 812.
  • the residual noise reduction process is performed wherein, in step 818, if the processed noise signal is within a threshold, control advances to step 820 wherein the processed noise is subjected to replacement, for example, a decay.
  • the residual noise reduction process may not be suitable in some applications where the application is negatively effected.
  • the Inverse Fourier Transform is generated in step 902 on the basis of the recovered noise processed audio signal recovered in step 904 and stored in step 900.
  • the time-domain signals are overlayed in order to regenerate the audio signal substantially without noise.
  • the present invention may be practiced as a software application, preferably written using C or any other programming language, which may be embedded on, for example, a programmable memory chip or stored on a computer-readable medium such as, for example, an optical disk, and retrieved therefrom to drive a computer processor.
  • Sample code representative of the present invention is illustrated in Appendix A which, as will be appreciated by those skilled in the art, may be modified to accommodate various operating systems and compilers or to include various bells and whistles without departing from the spirit and scope of the present invention.
  • a spectral subtraction system that has a simple, yet efficient mechanism, to estimate the noise magnitude spectrum even in poor signal to noise ratio situations and in continuous fast speech cases.
  • An efficient mechanism is provided that can perform the magnitude estimation with little cost, and will overcome the problem of phase association.
  • a stable mechanism is provided to estimate the noise spectral magnitude without the smearing of the data.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Quality & Reliability (AREA)
  • Circuit For Audible Band Transducer (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)

Abstract

L'invention se rapporte à un système conçu pour supprimer et réduire un bruit de signal sonore issu de sources de bruits électriques et électromagnétiques telles qu'un convertisseur de puissance C.A.-C.C. utilisé par des ordinateurs. Le système reçoit un signal sonore numérique (102) échantillonné à une fréquence qui possède au moins deux fois la largeur de bande du signal sonore. Les échantillons d'entrée sont stockés dans un tampon temporaire de 256 points (104). Lorsque le tampon est plein, les 256 nouveaux points sont combinés dans un combineur (106) avec les 256 points précédents de manière à fournir 512 points qui sont multipliés par un multiplicateur (108) associé à une fenêtre pour rendu réaliste ayant une longueur de 512 points. Les résultats obtenus sont convertis en un domaine de fréquence au moyen d'un processeur (110) à transformée de Fourier rapide (FFT). La sortie du processeur FFT est traitée dans une unité de traitement du bruit (112) qui effectue l'estimation d'amplitude du bruit pour chaque secteur de fréquences, la soustraction qui permet d'estimer la valeur complexe exempte de bruit pour chaque secteur de fréquences et le processus de réduction du bruit résiduel.
PCT/US2000/003538 1999-02-18 2000-02-11 Systeme, procede et appareil de suppression du bruit WO2000049602A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
CA002358710A CA2358710A1 (fr) 1999-02-18 2000-02-11 Systeme, procede et appareil de suppression du bruit
JP2000600263A JP2002537586A (ja) 1999-02-18 2000-02-11 雑音を消去するためのシステム、方法及び装置
IL14398900A IL143989A0 (en) 1999-02-18 2000-02-11 System, method and apparatus for cancelling noise
EP00908595A EP1157376A1 (fr) 1999-02-18 2000-02-11 Systeme, procede et appareil de suppression du bruit

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US09/252,874 1999-02-18
US09/252,874 US6363345B1 (en) 1999-02-18 1999-02-18 System, method and apparatus for cancelling noise
US38599699A 1999-08-30 1999-08-30
US09/385,996 1999-08-30

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WO2003003349A1 (fr) * 2001-06-28 2003-01-09 Oticon A/S Procede de reduction de bruit et ensemble de microphones pour reduire le bruit
EP2228910A2 (fr) * 2009-03-13 2010-09-15 EADS Deutschland GmbH Procédé de distinction entre des bruits et des signaux utiles
EP2670165A3 (fr) * 2008-08-29 2014-04-16 Dev-Audio Pty Ltd Système de réseau de microphones et procédé d'acquisition sonore
US9392360B2 (en) 2007-12-11 2016-07-12 Andrea Electronics Corporation Steerable sensor array system with video input
US9508358B2 (en) 2010-12-15 2016-11-29 Koninklijke Philips N.V. Noise reduction system with remote noise detector
US10015598B2 (en) 2008-04-25 2018-07-03 Andrea Electronics Corporation System, device, and method utilizing an integrated stereo array microphone

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JP4527654B2 (ja) * 2005-11-24 2010-08-18 Ｎｅｃアクセステクニカ株式会社 音声通信装置
CN1822092B (zh) * 2006-03-28 2010-05-26 北京中星微电子有限公司 一种消除语音输入中背景噪声的方法及其装置
WO2007130765A2 (fr) * 2006-05-04 2007-11-15 Sony Computer Entertainment Inc. Annulation d'écho et de bruit
WO2010079526A1 (fr) * 2009-01-06 2010-07-15 三菱電機株式会社 Dispositif et programme d'annulation de bruit
CN104734736B (zh) * 2013-12-19 2018-06-22 南京航空航天大学 一种采用自适应滤波器消除开关电源开关噪声的实现方法
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CA2358710A1 (fr) 2000-08-24
CN1348583A (zh) 2002-05-08

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