EP2495724B1 - Procédé et dispositif destinés à l'évaluation d'un bruit parasite - Google Patents

Procédé et dispositif destinés à l'évaluation d'un bruit parasite Download PDF

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Publication number
EP2495724B1
EP2495724B1 EP12154134.6A EP12154134A EP2495724B1 EP 2495724 B1 EP2495724 B1 EP 2495724B1 EP 12154134 A EP12154134 A EP 12154134A EP 2495724 B1 EP2495724 B1 EP 2495724B1
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Prior art keywords
noise
time window
interference noise
estimated value
current time
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EP12154134.6A
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German (de)
English (en)
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EP2495724A1 (fr
Inventor
Tobias Rosenkranz
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Sivantos Pte Ltd
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Siemens Medical Instruments Pte Ltd
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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
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L21/0232—Processing in the frequency domain
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R25/00—Deaf-aid sets, i.e. electro-acoustic or electro-mechanical hearing aids; Electric tinnitus maskers providing an auditory perception
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R25/00—Deaf-aid sets, i.e. electro-acoustic or electro-mechanical hearing aids; Electric tinnitus maskers providing an auditory perception
    • H04R25/50—Customised settings for obtaining desired overall acoustical characteristics
    • 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
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02163—Only one microphone
    • 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
    • G10L21/06—Transformation of speech into a non-audible representation, e.g. speech visualisation or speech processing for tactile aids
    • G10L2021/065—Aids for the handicapped in understanding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2225/00—Details of deaf aids covered by H04R25/00, not provided for in any of its subgroups
    • H04R2225/43—Signal processing in hearing aids to enhance the speech intelligibility

Definitions

  • the present invention relates to a method of estimating a noise by providing a value for the power density of an overall signal including a useful signal and the noise to be estimated in a current time window, comparing the value of the overall signal with an amplification factor-multiplied estimated noise value a time window preceding the current time window, and using the smaller of the two values of the comparison as a prediction value for the noise in the current time window.
  • the present invention relates to a device for estimating a noise of an input device for providing the value of the power density of the total signal and a recursive minimum estimator for comparing the value of the total signal with the estimate of the preceding time window.
  • the present invention also relates to a hearing device with such a device for estimating a noise.
  • a hearing device here means any sound-emitting device that can be worn in or on the ear, in particular a hearing device, a headset, headphones and the like.
  • Hearing aids are portable hearing aids that are used to care for the hearing impaired.
  • different types of hearing aids such as behind-the-ear hearing aids (BTE), hearing aid with external handset (RIC: receiver in the canal) and in-the-ear hearing aids (IDO), for example Concha hearing aids or canal hearing aids (ITE, CIC).
  • BTE behind-the-ear hearing aids
  • RIC hearing aid with external handset
  • IDO in-the-ear hearing aids
  • ITE concha hearing aids or canal hearing aids
  • the hearing aids listed by way of example are worn on the outer ear or in the ear canal.
  • bone conduction hearing aids, implantable or vibrotactile hearing aids are also available on the market.
  • the stimulation of the damaged hearing takes place either mechanically or electrically.
  • Hearing aids have in principle as essential components an input transducer, an amplifier and an output transducer.
  • the input transducer is usually a sound receiver, z. As a microphone, and / or an electromagnetic receiver, for. B. an induction coil.
  • the output transducer is usually used as an electroacoustic transducer, z. As miniature speaker, or as an electromechanical transducer, z. B. bone conduction, realized.
  • the amplifier is usually integrated in a signal processing unit. This basic structure is in FIG. 1 shown using the example of a behind-the-ear hearing aid. In a hearing aid housing 1 for carrying behind the ear, one or more microphones 2 for receiving the sound from the environment are installed. A signal processing unit 3, which is also integrated in the hearing aid housing 1, processes the microphone signals and amplifies them.
  • the output signal of the signal processing unit 3 is transmitted to a loudspeaker or earpiece 4, which outputs an acoustic signal.
  • the sound is optionally transmitted via a sound tube, which is fixed with an earmold in the ear canal, to the eardrum of the device carrier.
  • the power supply of the hearing device and in particular the signal processing unit 3 is effected by a likewise integrated into the hearing aid housing 1 battery. 5
  • the useful signal which is usually language
  • stationary noise usually does not pose a major problem for speech enhancement systems of known type
  • non-stationary noise is usually more of a challenge.
  • Particularly affected are single-channel (ie, a single microphone is used), model-based speech enhancement systems that are designed to suppress even very transient noises.
  • Such single-channel speech enhancement systems can relieve the listener by attenuating noise accordingly.
  • Wiener filters When creating a Wiener filter, it is necessary to estimate at least the spectral noise power density (PSD).
  • PSD spectral noise power density
  • Conventional speech enhancement systems typically require the noise to be more stationary, i. H. the characteristic of the noise changes only slowly as a function of time. Thus, the noise characteristics during speech pauses can be estimated, but this requires robust voice activity detection (VAD).
  • VAD voice activity detection
  • minima are able to update the noise estimate even during voice activity and thus do not require VAD.
  • minimum statistics noisy speech is broken down into subbands, and minima are searched for in those subbands at a certain time interval. Because of the high dynamics of the speech signal, the minima should correspond to the spectral noise power density if the noise or noise is sufficiently stationary. The minima are used as inputs for setting a gain factor in the respective frequency band.
  • the method fails if the noise exceeds a certain degree of unstationarity. This means that its performance breaks down in very unsteady environments (eg chatter in a cafeteria).
  • codebook-based speech enhancement techniques use a prior knowledge of speech and noise.
  • the main idea is to estimate the spectral envelope and wideband signal powers (gains) of speech and noise from the distorted signal.
  • Typical spectral envelopes of speech and different noise classes are stored in codebooks.
  • the optimal gain factors i.e., the wideband speech power and the wideband noise power
  • the criterion is that the sum of the speech and noise codebook entries corresponds as far as possible to the current disturbed signal.
  • a second step either the pair (along with the associated estimated gain factors) that most likely corresponds to the current disturbed spectrum is selected, or each pair is weighted with the probability that it corresponds to the current disturbed sound spectrum, and all so weighted couples are summed up.
  • This provides estimates of the speech and noise components of the disturbed sound spectrum. These estimates are used as input to subsequent noise reduction, such as through a "Wiener filter”.
  • This estimation procedure is performed in short time windows (eg 8 ms) so that rapid changes in the noise characteristic can be followed almost instantaneously. A minimum statistic estimator can only follow such changes with a delay in the range of a few seconds.
  • the noise estimate is limited to a predefined set of codebook entries. Since these entries represent spectral envelopes, they are smoothed along the frequency axis. This means, for example, that sharp spectral peaks are not modeled.
  • the ability of the codebook-based approach to respond instantaneously to noise changes means that the estimate varies widely. Since the estimation of the broadband level is by nature not perfect and therefore fluctuates relatively strongly around the true value, unpleasant artifacts occur in the noise-free signal.
  • this codebook-based approach can not handle noise classes that have not been trained.
  • the object of the present invention is therefore to propose a method and a device with which it is possible to be able to estimate unknown noise as quickly as possible.
  • the "recursive minimum tracking" is combined with the "codebook-based noise estimation” in order to achieve an improved reduction of non-stationary noise.
  • the above-mentioned disadvantages of the recursive minimum search as The disadvantages of the codebook-based estimation taken by itself are essentially eliminated.
  • the value of the total signal and the estimate of a noise are each spectral values.
  • the signal processing in the method according to the invention is then carried out in the spectral range.
  • the method is used in parallel in a plurality of frequency channels.
  • the input signal is advantageously decomposed in a filter bank into the individual spectral components.
  • the estimated value for the noise in the current time window is smoothed with the estimated value from the preceding time window. This is favorable in that then no excessive jumps occur in the noise reduction.
  • the codebook estimate can be set to zero. Equivalent to this is when the codebook estimator is turned off. This makes the whole algorithm less sensitive to the fact whether the noise is known or not.
  • the above-described method of estimating noise is used for reducing noise.
  • a method for reducing noise is used for operating a hearing device or is implemented in a hearing device.
  • hearing aid wearers can benefit from the improved, combined noise reduction method.
  • the above-mentioned noise-estimating apparatus may be integrated into a hearing apparatus.
  • This hearing device can be designed as a hearing aid.
  • a microphone 10 of the hearing aid supplies a noisy or disturbed signal x (k).
  • This signal is spectrally decomposed into individual frequency bands by means of a filter bank 11.
  • This is a spectral signal X (e j ⁇ ) ready.
  • This spectral signal is supplied to a noise estimation unit 12, which obtains therefrom an estimated value ⁇ nn (e j ⁇ ) for the noise power density .
  • a noise reduction filter 13 determines therefrom spectral weights ⁇ (e j ⁇ ) In a multiplier 14, the weights ⁇ (e j ⁇ ) are then multiplied by the spectrum X (e j ⁇ ) of the total signal, resulting in an estimated value ⁇ (e j ⁇ ) for the useful signal (e.g. B. pure speech signal) arises.
  • An inverse filter bank 15 produces an estimate ⁇ (k) of the useful signal in the time domain.
  • the noise estimation in the noise estimation unit 12 is now optimized.
  • a noise estimation algorithm based on recursive minimum statistics and an algorithm based on one or more codebooks are combined for this purpose.
  • a codebook-based algorithm is used, as described in the article by T. Rosenkranz described at the outset.
  • the noise estimate of the codebook based algorithm is integrated into the recursive estimation algorithm based on the minimum statistics similar to the algorithm of Eberhard Hänsler and Gerhard Schmidt mentioned earlier.
  • FIG. 3 presented a model of a recursive noise estimator.
  • the method shown there takes place in several frequency (sub) bands independently of each other.
  • the individual frequency bands are, for example, with the in FIG. 2 obtained filter bank 11 won.
  • X is, for example, a periodogram of noisy speech.
  • the output signal ⁇ nn (e j ⁇ ) corresponds to an estimate of the noise power spectrum .
  • the input signal is smoothed in a smoothing unit 16.
  • the smoothed input spectrum is compared in a comparator 17 with the estimated noise spectrum of a previous window.
  • the estimated interference power spectrum of the preceding time window is multiplied in advance by a constant "noise estimate gain" which corresponds to the value 1 + ⁇ , where ⁇ ⁇ 1.
  • the amplifier 18 is provided. It receives its input signal from a delay element 19, which in turn is fed by the interference estimate ⁇ nn (e j ⁇ ) of the current time window.
  • the estimated value of the preceding time window (signal after the delay unit 19) is subtracted from the output signal of the comparator 17 in a subtracter 20.
  • the difference signal is multiplied by a constant in a further amplifier 21.
  • the resulting signal is finally added in an adder 22 with the estimate of the preceding time window, which finally results in the smoothed estimate ⁇ nn (e j ⁇ ).
  • the minimum of the two signals (in the current time window and in the preceding time window) is used. It is thus a kind of efficient implementation of the minimum statistics algorithm according to the article by R. Martin.
  • the behavior of this known estimator comes from the graph of FIG. 5 out.
  • the curve 23 shows the actual noise present. For example, it is street noise with fast passing cars.
  • the estimates are determined from a mixture of this noise with a speech signal at a distance (SNR) of 0 dB.
  • the curve 24 shows the estimation of the recursive minimum tracking algorithm. For example, as can be seen from the first two seconds of the estimate, the estimator can not follow the rapid increase in noise.
  • the slope of the estimator is limited by the constant ⁇ . This constant ⁇ must be small, otherwise the estimate would follow the noisy input spectrum too quickly and speech components are erroneously included in the noise estimate.
  • FIG. 4 The signal flow diagram shown corresponds essentially to that of FIG. 3 , Therefore, the description of FIG. 3 Referenced.
  • a codebook-based noise estimate is integrated into the estimator by a maximum operation in a second comparator unit 27 (logic device) immediately after the comparator unit 17 with the minimum operation.
  • the comparison unit 27 obtains a codebook estimate ⁇ nnCB from an in FIG. 4 not shown codebook estimation device.
  • the codebook based estimate is taken.
  • the recursive part of the algorithm is then able to track the noise from a higher level.
  • the combined algorithm of the present invention therefore, can respond to changes in noise level as quickly as codebook estimates.
  • FIG. 5 shows this behavior of the combined estimate.
  • the codebook estimate is shown by curve 25.
  • the estimate of the combined algorithm is shown by curve 26.
  • the combined estimate 26 follows the increase of the noise floor with a very small delay due to the smoothing part 20, 21, 22 of the algorithm.
  • the inventive combined algorithm provides a better estimate when the codebook based estimate 25 underestimates the actual noise 23. Namely, in the time range between 4 and 6 seconds, the codebook estimate 25 is significantly lower than the actual noise.
  • the combined estimate 26 is much closer to the real noise than the codebook based estimate 25 or the recursive estimate 24 alone.
  • a codebook-based noise estimate is combined with a recursive noise estimate.
  • the benefits of each of these estimates are gained for the combination, while the disadvantages are minimized.
  • the advantages of the combination are that the combined algorithm can track fast noise fluctuations much faster than conventional recursive noise estimators. Another advantage is that by injecting the codebook based estimation algorithm in the proposed manner, the estimator becomes a conventional recursive estimator when the codebook based estimation is turned off or set to zero. This in turn improves the robustness of the algorithm. Further, an advantage of the proposed combination is that the algorithm may continue to track the noise if the codebook based algorithm underestimates the actual noise floor. The combined algorithm can therefore bridge areas in which the codebook-based estimate either underestimates or shuts down the noise. In addition, the noise estimate varies significantly less than the codebook-based estimate alone, resulting in much more pleasing sound reproduction with reduced artifacts. In addition, the proposed estimator can handle noise for which the codebook-based algorithm has not been trained. This is due to the recursive part of the algorithm, which is independent of the codebook-based estimate.

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Acoustics & Sound (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Otolaryngology (AREA)
  • Neurosurgery (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Quality & Reliability (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • Noise Elimination (AREA)

Claims (10)

  1. Procédé d'évaluation d'un bruit parasite par
    - mise à disposition d'une valeur de la densité de puissance d'un signal global qui contient un signal utile et le bruit parasite à évaluer dans un créneau temporel présent,
    - comparaison de la valeur du signal global à une valeur d'estimation d'un bruit parasite ( 18 ) multipliée par un facteur d'amplification en provenance d'un créneau temporel précédent le créneau temporel présent et
    - utilisation de la plus petite ( 17 ) des deux valeurs de la comparaison comme valeur d'estimation préalable de bruit parasite dans le créneau temporel présent,
    caractérisé par
    - mise à disposition d'une valeur d'évaluation par livret de code du bruit parasite dans le créneau temporel présent et
    - détermination et utilisation de la valeur ( 27 ) la plus grande de la valeur d'évaluation préalable et de la valeur d'évaluation par livret de code comme valeur d'évaluation du bruit parasite dans le créneau temporel présent.
  2. Procédé suivant la revendication 1, dans lequel la valeur du signal global et la valeur d'évaluation d'un bruit parasite sont respectivement des valeurs spectrales.
  3. Procédé suivant la revendication 1 ou 2, dans lequel on lisse ( 20, 21, 22 ) la valeur d'évaluation du bruit parasite dans le créneau temporel présent avec la valeur d'évaluation provenant du créneau temporel précédent.
  4. Procédé suivant l'une des revendications précédentes, dans lequel on met la valeur d'évaluation par livret de code à zéro.
  5. Procédé suivant l'une des revendications précédentes, que l'on applique en parallèle dans plusieurs canaux de fréquence.
  6. Procédé de réduction du bruit parasite par l'évaluation du bruit parasite conformément à l'une des revendications précédentes et réduction du bruit parasite suivant la valeur d'évaluation.
  7. Procédé pour faire fonctionner une prothèse auditive, dans lequel on réduit le bruit parasite suivant la revendication 6.
  8. Système d'évaluation d'un bruit parasite comprenant
    - un dispositif ( 11 ) d'entrée pour la mise à disposition d'une valeur de la densité de puissance d'un signal global, qui contient un signal utile et le bruit parasite à évaluer dans un créneau temporel présent,
    - un dispositif ( 17, 18, 19 ) d'évaluation minimum récursif pour la comparaison de la valeur du signal global à une valeur d'évaluation d'un bruit parasite, multipliée par un facteur d'amplification d'un bruit parasite provenant d'un créneau temporel précédent le créneau temporel présent et pour l'émission de la plus petite des deux valeurs de la comparaison comme valeur d'évaluation préalable du bruit parasite dans le créneau temporel présent,
    caractérisé par
    - un dispositif d'évaluation par livret de code pour la mise à disposition d'une valeur d'évaluation par livret de code du bruit parasite dans le créneau temporel présent et
    - un dispositif ( 27 ) logique pour la détermination de la valeur la plus grande de la valeur d'évaluation préalable et de la valeur d'évaluation par livret de code comme valeur d'évaluation du bruit parasite dans le créneau temporel présent.
  9. Prothèse auditive dans laquelle est intégré un dispositif suivant la revendication 8 d'évaluation d'un bruit parasite.
  10. Prothèse auditive suivant la revendication 9, qui est constituée en appareil auditif.
EP12154134.6A 2011-02-17 2012-02-07 Procédé et dispositif destinés à l'évaluation d'un bruit parasite Active EP2495724B1 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE102011004338A DE102011004338B3 (de) 2011-02-17 2011-02-17 Verfahren und Vorrichtung zum Schätzen eines Störgeräusches

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EP2495724A1 EP2495724A1 (fr) 2012-09-05
EP2495724B1 true EP2495724B1 (fr) 2013-11-20

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US (1) US8634581B2 (fr)
EP (1) EP2495724B1 (fr)
DE (1) DE102011004338B3 (fr)
DK (1) DK2495724T3 (fr)

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Publication number Priority date Publication date Assignee Title
US9763016B2 (en) * 2014-07-31 2017-09-12 Starkey Laboratories, Inc. Automatic directional switching algorithm for hearing aids
DE102015201073A1 (de) 2015-01-22 2016-07-28 Sivantos Pte. Ltd. Verfahren und Vorrichtung zur Rauschunterdrückung basierend auf Inter-Subband-Korrelation
WO2017218386A1 (fr) 2016-06-13 2017-12-21 Med-El Elektromedizinische Geraete Gmbh Estimation de puissance de bruit récursive à adaptation de modèle de bruit
CN114567846B (zh) * 2017-02-09 2024-09-24 奥迪康有限公司 一种助听器装置
CN113008361B (zh) * 2021-02-04 2023-08-15 国网湖南省电力有限公司 一种变电站站界噪声抗环境干扰检测方法及装置

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ATE425532T1 (de) * 2006-10-31 2009-03-15 Harman Becker Automotive Sys Modellbasierte verbesserung von sprachsignalen
US8385572B2 (en) * 2007-03-12 2013-02-26 Siemens Audiologische Technik Gmbh Method for reducing noise using trainable models
WO2009043066A1 (fr) * 2007-10-02 2009-04-09 Akg Acoustics Gmbh Procédé et dispositif pour une amélioration de parole monocanal basée sur un modèle auditif à faible latence
DE102008017550A1 (de) 2008-04-07 2009-10-08 Siemens Medical Instruments Pte. Ltd. Mehrstufiges Schätzverfahren zur Störgeräuschreduktion und Hörvorrichtung

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US20120213395A1 (en) 2012-08-23
DK2495724T3 (da) 2014-02-10
US8634581B2 (en) 2014-01-21
DE102011004338B3 (de) 2012-07-12
EP2495724A1 (fr) 2012-09-05

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