EP0948237A2 - Procédé pour la suppression du bruit dans un signal de microphone - Google Patents

Procédé pour la suppression du bruit dans un signal de microphone Download PDF

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Publication number
EP0948237A2
EP0948237A2 EP99106123A EP99106123A EP0948237A2 EP 0948237 A2 EP0948237 A2 EP 0948237A2 EP 99106123 A EP99106123 A EP 99106123A EP 99106123 A EP99106123 A EP 99106123A EP 0948237 A2 EP0948237 A2 EP 0948237A2
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EP
European Patent Office
Prior art keywords
signal
filter
speech
filter function
microphone
Prior art date
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
Application number
EP99106123A
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German (de)
English (en)
Other versions
EP0948237B1 (fr
EP0948237A3 (fr
Inventor
Hans-Jörg Thomas
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Harman Becker Automotive Systems GmbH
Original Assignee
DaimlerChrysler Aerospace AG
Harman Becker Automotive Systems GmbH
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
Application filed by DaimlerChrysler Aerospace AG, Harman Becker Automotive Systems GmbH filed Critical DaimlerChrysler Aerospace AG
Publication of EP0948237A2 publication Critical patent/EP0948237A2/fr
Publication of EP0948237A3 publication Critical patent/EP0948237A3/fr
Application granted granted Critical
Publication of EP0948237B1 publication Critical patent/EP0948237B1/fr
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

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Classifications

    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers
    • H04R3/007—Protection circuits for transducers
    • 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
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02168—Noise filtering characterised by the method used for estimating noise the estimation exclusively taking place during speech pauses

Definitions

  • the invention relates to a method for eliminating interference Microphone signal.
  • Such methods are particularly useful for voice input of commands and / or for hands-free phones increasingly of importance, especially the situation in is an important application for a vehicle.
  • a playback device such as e.g. a radio, a Cassette or CD player through a loudspeaker Generated noise environment, which as a noise signal from one Microphone recorded voice signal, for example for voice recognition or telephone transmission is superimposed.
  • a playback device such as e.g. a radio, a Cassette or CD player through a loudspeaker Generated noise environment, which as a noise signal from one Microphone recorded voice signal, for example for voice recognition or telephone transmission is superimposed.
  • a playback device such as e.g. a radio, a Cassette or CD player through a loudspeaker Generated noise environment, which as a noise signal from one Microphone recorded voice signal, for example for voice recognition or telephone transmission is superimposed.
  • That from a source of interference, especially a loudspeaker outgoing interference signal does not only arrive at the shortest direct Way to the microphone, but also occurs via numerous Reflections as an overlay of a plurality of Echoes with different delay times in the microphone signal in Appearance.
  • the total exposure to the interference signal from the Interference source on the microphone signal can by a priori unknown transfer function of the room, for example of the passenger compartment of a motor vehicle are described.
  • the transfer function changes depending on the occupation of the Vehicle and according to the position of the individual.
  • a compensation signal can be generated, which by Subtraction from the microphone signal is freed from the interference signal Signal, for example a pure voice signal.
  • the replica mentioned represents a more or less good approximation to the unknown transfer function and the malfunction cannot be completely eliminated become.
  • the object of the present invention is a method to provide interference from a microphone signal that at reasonable signal processing effort good properties with regard to interference suppression.
  • the Compensation of the interference signal component in the microphone signal by means of one from the reference signal via the simulation of the Transfer function generated compensation signal in the frequency domain is made so that microphone signal, compensation signal and output signal in the frequency domain, i.e. are in the form of spectra.
  • the signal processing required in this step in the frequency domain a spectral transformation of the microphone signal, but takes into account that the simulation of the transfer function is more advantageous in the frequency domain and provides for an advantageous subsequent additional noise reduction of the output signal, which is also typically in the frequency domain is already a special one suitable waveform ready.
  • Trouble shooting proves to be particularly advantageous of a speech signal based on a setting of the Replica filters in a previous language break won and saved.
  • the division of the replica filter into several sub-filters and the interference clearance based on one in one Speech pause filter settings are also independent of interference signal compensation in the frequency domain independently for the interference suppression of a microphone signal feasible and advantageous.
  • the loudspeaker signal x is filtered by the a priori unknown transfer function G of the vehicle interior.
  • the interference component r arises, which is added to the microphone signal y with the speech signal s.
  • an estimate r ⁇ is generated from the loudspeaker signal x using the filter simulation H.
  • the voice signal can still contain interference in the form of, for example, engine noise or external noise, but these are not dealt with explicitly in this context.
  • H is an adaptive filter and works according to one in the Literature known standard method, the LMS algorithm (least mean squares).
  • the error signal E needed to adapt the coefficient to accomplish in filter H.
  • the output signal s ⁇ fed to the determination of the filter coefficients.
  • the adaptive system H can e.g. in the time domain as FIR filter (finite impulse response filter) will be realized. With long impulse response lengths, as they often occur in practice however, this requires a very high computing effort.
  • FIR filter finite impulse response filter
  • FLMS frequency domain
  • F is a spectral transformation FFT of a time signal into the frequency domain and F -1 is the inverse IFFT.
  • the processing steps designated as projections P1, P2 and P3 are used for the correct segmentation of the data by the block-wise use with the FFT or IFFT and are explained in more detail later.
  • the filter works by multiplying the reference spectrum X by the filter coefficient vector H.
  • the spectrum of the filter output R ⁇ is transformed back into the time domain via F -1 .
  • the signal r ⁇ is available.
  • the projection P1 which is particularly complex here with two spectral transformations, calculates from H 'the coefficient vector H required for the filtering.
  • the spectrum S ⁇ of the output signal evaluated with P 3 is used to calculate the correction vector ⁇ H' s + r - r ⁇ needed.
  • FIG. 3 A detailed block diagram of the FLMS algorithm shown in FIG. 2b is shown in FIG. 3.
  • the sample values of a signal and the reference points of the FFT are commonly referred to as samples. All spectral transformations and their inverses are to be segmented as 256-point FFTs, each overlapping by 128 samples.
  • the output signal s ⁇ is made up of 128 sample blocks in the time domain. It arises from the difference between the second block halves (that is, samples 129 to 256) of the microphone signal and the filtered compensation signal r ⁇ .
  • the projection P1 is complex, which requires 2 FFTs and converts the vector H 'into the vector H.
  • the first half (samples 1 to 128) is cut out of the complex 256-point result vector of the backward transformation from the frequency to the time domain (IFFT) and the second half (samples 129 to 256) is set to zero.
  • the transformation into the frequency domain is carried out again using FFT.
  • the projection P2 is simple. It consists of the sectioning of the last 128 samples already described above, which again results in non-overlapping 128-sample blocks from overlapping 256-sample blocks.
  • the projection P3 is also very simple, which in turn provides overlapping 256-sample blocks from non-overlapping 128-sample blocks of the output signal by prepending 128 zero values.
  • the adaptation of the filter coefficients H ' L + 1 for a cycle L + 1 consists of the addition of a renewal vector ⁇ H' L to the old coefficient vector H ' L.
  • the spectrum X of the reference signal is stored in a buffer D delayed by 1 or 2 block lengths and the undelayed X1 and the two delayed spectra X2, X3 separately in with an extended projection P1 multiplied certain coefficient vectors H1, H2, H3.
  • the coefficient vectors are formed analogously to Case of only a partial filter, whereby in K1, K2, K3 each associated reference spectrum with the spectrum S ⁇ of the output signal is linked. The effort is mainly by tripling the P1 projection considerably elevated. Additional space requirements will be necessary the spectra of the older one by 1 or 2 block lengths To provide reference signals X.
  • Figure 6 provides a more detailed block diagram of the FLMS algorithm with frequency domain output signal and allows a comparison with FIG. 3 again (time domain output).
  • the filter adaptation has remained unchanged consisting of smoothing the spectral power, power normalization and coefficient renewal. They are new FFT in the microphone channel, the difference Y-R ⁇ in the frequency instead of in the time domain for the output formation, and finally the newly defined projection P4, which is only through the complementary time window of the projection P1 differs.
  • the FLMS algorithm is shown with 3 sub-filters (384-sample impulse response), which has a sufficient suppression of the radio signal in the microphone channel of the speech input system.
  • the Projections P1 and P4 are shown in simplified form. It is the additional effort already known from FIG. 4b in the form the memory P and the tripling of the projection P1 evident.
  • the 1-part filter solution according to Fig. 6 becomes the sum W of the current and the two in time previous reference power spectra on the Given the input of the recursive filter.
  • the fact that at the filter output now practically 3 times the spectral smoothed Performance is available after the reciprocal by multiplying by the constant 6 ⁇ .
  • the filter adaptation is now the output spectrum S ⁇ for the 3 coefficient vectors of the 3 sub-filters separately carried out.
  • FIG. 9 An example Z0 for the operation of the invention according to Figure 7 shows Figure 9.
  • the input data has been synthesized generated.
  • the microphone signal Y was created by Convolution of this noise signal with a likewise constructed one 384-sample impulse response and the addition of one extremely weak speech signals.
  • the 10 spoken Digits just in color (because filtered) Recognizing noise When listening to this in 9 signal y recorded above are the 10 spoken Digits just in color (because filtered) Recognizing noise. That transformed back into the time domain
  • Output signal of the estimator frees up after a approx. 1 second (12000 samples) settling process very effective the speech input from the noise and delivers an undistorted but slightly reverberated speech signal S ⁇ (Fig. 9 below).
  • this came from real measurements in the vehicle Reference signal tapped at the radio speaker terminals radio and that recorded by the microphone of the voice input system Signal micro of scene Z1.
  • This microphone signal is shown in Fig. 11 above, consists of 100000 samples and therefore has a sampling frequency of 12 kHz a duration of approximately 8.3 seconds. It is about to fluent and relatively fast spoken language vehicle occupants sitting in the rear right while at the same time music with normal volume from the car radio speaker sounds. After applying the interference suppression measure 7 and conversion into the time range the output signal shown in Fig. 11 below. Of the Hearing test shows a clear elaboration of the language component or a remarkable one especially in the short language breaks Music suppression.
  • a suitable one Feature serves as an indicator along with a threshold for voice input. Falls below the characteristic the threshold, so this is a sign of missing Voice input.
  • the filter coefficient set is now used resorted to the immediately before the Threshold crossing - i.e. at the end of the previous one Speech pause - was saved.
  • This saved Coefficients H10, H20, H30 usually provide a clear better radio signal compensation than that under the disturbing influence of voice input is constantly changing current coefficients H, H2, H3.
  • Fig. 8 shows an embodiment with a further improved FLMS processing with 3 partial filters.
  • existing current filter coefficient vectors H1, H2, H3, which were continuously adopted to form the Output signal y-R were required, there is now an additional one Output signal (y-Ro) that is stored using Coefficients H10, H20, H30 is formed.
  • the current coefficient sets H1, H2, H3 only provide missing speech input in the steady state usable compensation filter in the frequency domain, on the other hand provide inadequate filter properties for voice input, because the adaptation process in the control loop is constantly disturbed. If there is no voice input, i.e.
  • the outputs (y-Ro) and (y-Ra) are identical. Inserting Voice inputs open the 3 switches, whereby the last ones in the memories M1, M2, M3 Coefficients H10, H20, H30 no longer overwritten will and remain unchanged. This state in which the outputs (Y-Ro) and (Y-Ra) differ hold until a speech pause is detected again and the switches are closed.
  • the smoothed sum has become the speech pause feature fea all absolute values of the coefficient correction vectors ⁇ H1 ', ⁇ H2 ', ⁇ H3' proven (Fig. 8a).
  • This size is zero or has small numerical values if there is none or only there is little need to change the coefficients. This is the case during breaks in speech, the control loop is practical steady.
  • Disorders such as those caused by voice input - but also by movements of the vehicle occupants - have an increased need for readjustment result, which is characterized by correspondingly large numerical values noticeable with ⁇ H1 ', ⁇ H2', ⁇ H3 'and thus with the characteristic fea makes.
  • a smoothing filter for example, a recursive one 1st order low pass with the feat input on his Output the smoothed speech pause feature fea is available, which after comparison with a threshold value th the Switch for coefficient acceptance controls.
  • the 384 sample impulse response measured at the end of the scene in FIG with associated amount transfer function 15 as the current impulse response (a) or current Transfer function (b) shown.
  • the estimate from the current coefficient H1, H2, H3 is from the saved Coefficients H10, H20, H30 an impulse response (c) and a transfer function (d) of high quality can be calculated.
  • the impulse response from the stored coefficients points the typical zero samples at the beginning, which are indicated by the Running time of the direct sound from the radio speaker to Voice input microphone. From the example readable dead time of approx. 40 samples the distance between the speaker and the microphone determine.
  • Fig. 16a the "right-hand" 128-sample rectangular window in the time domain
  • Fig. 16b the ideal projection replaced by a 128-sample Hamming window
  • Fig. 17 shows, the real part of the spectrum exists in the rectangular window from a single line (DC component), while the middle antisymmetric imaginary part spectrum from many lines slowly descending towards the outside with alternating lines Zeros exist.
  • the projection P1 can of course also be used (IFFT - left-sided rectangular window - FFT) replace with a corresponding convolution operation in the frequency domain the conjugate complex 7-line spectrum.
  • IFFT - left-sided rectangular window - FFT
  • Effortless solutions can be nevertheless achieve by following in the LMS algorithm 8 the 3 projections P1 not simultaneously in one 256-sample input data block must be processed.
  • the with 128-sample overlapping input data blocks of length 256 are numbered starting at "1" sketched in Fig. 19a. So it is e.g. possible at modulo-3-counting of the input data blocks the 3 sub-filter projections not in parallel (Fig.
  • the first of these scenes Z2 includes voice input from Digits, the radio speaker almost white Noise emits at a relatively high volume.
  • the associated 100000 sample microphone signal is in Fig. 20 above, the extracted output signal is shown in Fig. 20 below.
  • a clear release of noise from the output signal compared to the microphone input is made by listening comparison firmly.
  • the time course of the speech pause feature is up along with the constant threshold th Fig. 21 mapped and the derived language breaks or the assigned switch positions in Fig. 21 below.
  • FIG. 22 shows the in an analogous manner to FIG impulse response (a) and transfer function found at the end of the scene (b) based on the current coefficients and the corresponding sizes (c), (d) based on the Speech pause setting. It is clearly recognizable that the current impulse response found at the end of the scene Speech input represents disturbed result while the out the impulse response from the last speech pause stored coefficient sets has a high quality.
  • the first 100000 samples of a measuring scene Z3 with POP music on the radio and fluent to quickly spoken language of the The person sitting on the right rear is in the form of a microphone signal y recorded in Fig. 23 above. After about 10,000 samples (0.83 s) the radio signal is suppressed usably (Fig. 23 below). Even in the last third of this POP music suppression remains when voice input begins effectively preserved, making speech intelligibility noticeably improved here compared to the microphone signal becomes. After a long pause in speech, it comes because of the subsequent one non-stop voice input to one Falling below threshold (Fig. 24). This is why the impulse response recorded at the bottom of the scene in Fig.

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Computational Linguistics (AREA)
  • Quality & Reliability (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • Fittings On The Vehicle Exterior For Carrying Loads, And Devices For Holding Or Mounting Articles (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)
EP99106123A 1998-04-03 1999-04-01 Procédé pour la suppression du bruit dans un signal de microphone Expired - Lifetime EP0948237B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19814971 1998-04-03
DE19814971A DE19814971A1 (de) 1998-04-03 1998-04-03 Verfahren zur Störbefreiung eines Mikrophonsignals

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EP0948237A2 true EP0948237A2 (fr) 1999-10-06
EP0948237A3 EP0948237A3 (fr) 2006-02-08
EP0948237B1 EP0948237B1 (fr) 2008-06-11

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EP (1) EP0948237B1 (fr)
AT (1) ATE398326T1 (fr)
DE (2) DE19814971A1 (fr)

Cited By (11)

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Publication number Priority date Publication date Assignee Title
GB2392796A (en) * 2002-09-09 2004-03-10 Ford Global Tech Llc An audio noise cancellation apparatus for a sensor in an automative vehicle
WO2006130668A3 (fr) * 2005-06-01 2007-05-03 Bose Corp Surveillance de personne
EP1801788A1 (fr) * 2005-12-23 2007-06-27 QNX Software Systems (Wavemakers), Inc. Amélioration de signal périodique avancée
US7949520B2 (en) 2004-10-26 2011-05-24 QNX Software Sytems Co. Adaptive filter pitch extraction
US8170879B2 (en) 2004-10-26 2012-05-01 Qnx Software Systems Limited Periodic signal enhancement system
US8209514B2 (en) 2008-02-04 2012-06-26 Qnx Software Systems Limited Media processing system having resource partitioning
US8306821B2 (en) 2004-10-26 2012-11-06 Qnx Software Systems Limited Sub-band periodic signal enhancement system
US8543390B2 (en) 2004-10-26 2013-09-24 Qnx Software Systems Limited Multi-channel periodic signal enhancement system
US8694310B2 (en) 2007-09-17 2014-04-08 Qnx Software Systems Limited Remote control server protocol system
US8850154B2 (en) 2007-09-11 2014-09-30 2236008 Ontario Inc. Processing system having memory partitioning
US8904400B2 (en) 2007-09-11 2014-12-02 2236008 Ontario Inc. Processing system having a partitioning component for resource partitioning

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DE19958836A1 (de) * 1999-11-29 2001-05-31 Deutsche Telekom Ag Verfahren und Anordnung zur Verbesserung der Kommunikation in einem Fahrzeug
DE10041885A1 (de) * 2000-08-25 2002-03-07 Mueller Bbm Gmbh Sprachsignalübertragungssystem für Fahrzeuge
DE10052991A1 (de) * 2000-10-19 2002-05-02 Deutsche Telekom Ag Verfahren zur Ermittlung raumakustischer und elektroakustischer Parameter
DE10221990B4 (de) * 2002-05-17 2006-10-12 Audi Ag Reduzierung von Störgeräuschen an Autoradios mit Busanschlüssen
JP2005218010A (ja) * 2004-02-02 2005-08-11 Matsushita Electric Ind Co Ltd 車両用データ伝送システム
EP1848243B1 (fr) * 2006-04-18 2009-02-18 Harman/Becker Automotive Systems GmbH Système et procédé pour suppression d'echo multivoies
ATE445966T1 (de) * 2006-05-08 2009-10-15 Harman Becker Automotive Sys Echoverringerung für zeitvariante systeme
ATE436151T1 (de) * 2006-05-10 2009-07-15 Harman Becker Automotive Sys Kompensation von mehrkanalechos durch dekorrelation
EP1879181B1 (fr) * 2006-07-11 2014-05-21 Nuance Communications, Inc. Procédé pour la compensation des composants d'un signal audio dans un système de communication dans une voiture et un système pour ça
US20080063122A1 (en) * 2006-09-07 2008-03-13 Gwo-Jia Jong Method for suppressing co-channel interference from different frequency
EP1936939B1 (fr) * 2006-12-18 2011-08-24 Harman Becker Automotive Systems GmbH Annulation d'écho de faible complexité
US7577257B2 (en) * 2006-12-21 2009-08-18 Verizon Services Operations, Inc. Large scale quantum cryptographic key distribution network
US20080225688A1 (en) * 2007-03-14 2008-09-18 Kowalski John M Systems and methods for improving reference signals for spatially multiplexed cellular systems
EP1995940B1 (fr) * 2007-05-22 2011-09-07 Harman Becker Automotive Systems GmbH Procédé et appareil de traitement d'au moins deux signaux de microphone pour fournir un signal de sortie avec une réduction des interférences
ATE532324T1 (de) 2007-07-16 2011-11-15 Nuance Communications Inc Verfahren und system zur verarbeitung von tonsignalen in einem multimediasystem eines fahrzeugs
EP2222091B1 (fr) 2009-02-23 2013-04-24 Nuance Communications, Inc. Procédé pour déterminer un ensemble de coefficients de filtre pour un moyen de compensation d'écho acoustique
CN102043168B (zh) 2010-10-15 2012-11-07 中国石油化工股份有限公司 一种对数字信号进行仿真加噪的处理方法
JP6062861B2 (ja) * 2011-10-07 2017-01-18 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカＰａｎａｓｏｎｉｃ Ｉｎｔｅｌｌｅｃｔｕａｌ Ｐｒｏｐｅｒｔｙ Ｃｏｒｐｏｒａｔｉｏｎ ｏｆ Ａｍｅｒｉｃａ 符号化装置及び符号化方法
DE102014214143B4 (de) * 2014-03-14 2015-12-31 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Vorrichtung und Verfahren zum Verarbeiten eines Signals im Frequenzbereich
US20180166073A1 (en) * 2016-12-13 2018-06-14 Ford Global Technologies, Llc Speech Recognition Without Interrupting The Playback Audio
WO2018121972A1 (fr) * 2016-12-30 2018-07-05 Harman Becker Automotive Systems Gmbh Annulation d'écho acoustique
DE102017101497B4 (de) 2017-01-26 2020-08-27 Infineon Technologies Ag Mikro-Elektro-Mechanisches-System (MEMS) -Schaltkreis und Verfahren zum Rekonstruieren einer Störgröße
DE102018204687B3 (de) * 2018-03-27 2019-06-13 Infineon Technologies Ag MEMS Mikrofonmodul

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Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2392796A (en) * 2002-09-09 2004-03-10 Ford Global Tech Llc An audio noise cancellation apparatus for a sensor in an automative vehicle
US8543390B2 (en) 2004-10-26 2013-09-24 Qnx Software Systems Limited Multi-channel periodic signal enhancement system
US7949520B2 (en) 2004-10-26 2011-05-24 QNX Software Sytems Co. Adaptive filter pitch extraction
US8150682B2 (en) 2004-10-26 2012-04-03 Qnx Software Systems Limited Adaptive filter pitch extraction
US8170879B2 (en) 2004-10-26 2012-05-01 Qnx Software Systems Limited Periodic signal enhancement system
US8306821B2 (en) 2004-10-26 2012-11-06 Qnx Software Systems Limited Sub-band periodic signal enhancement system
US7525440B2 (en) 2005-06-01 2009-04-28 Bose Corporation Person monitoring
WO2006130668A3 (fr) * 2005-06-01 2007-05-03 Bose Corp Surveillance de personne
EP1801788A1 (fr) * 2005-12-23 2007-06-27 QNX Software Systems (Wavemakers), Inc. Amélioration de signal périodique avancée
US8850154B2 (en) 2007-09-11 2014-09-30 2236008 Ontario Inc. Processing system having memory partitioning
US8904400B2 (en) 2007-09-11 2014-12-02 2236008 Ontario Inc. Processing system having a partitioning component for resource partitioning
US9122575B2 (en) 2007-09-11 2015-09-01 2236008 Ontario Inc. Processing system having memory partitioning
US8694310B2 (en) 2007-09-17 2014-04-08 Qnx Software Systems Limited Remote control server protocol system
US8209514B2 (en) 2008-02-04 2012-06-26 Qnx Software Systems Limited Media processing system having resource partitioning

Also Published As

Publication number Publication date
US6895095B1 (en) 2005-05-17
EP0948237B1 (fr) 2008-06-11
DE19814971A1 (de) 1999-10-07
ATE398326T1 (de) 2008-07-15
EP0948237A3 (fr) 2006-02-08
DE59914782D1 (de) 2008-07-24

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