EP2495724B1 - Method and device for estimating an interference noise - Google Patents

Method and device for estimating an interference noise 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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German (de)
French (fr)
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EP2495724A1 (en
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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 OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal 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 OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal 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 OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal 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.

Abstract

The method involves obtaining power density value of noise signal output from the hearing device. The total noise signal value is compared with preset interference value from current time window by multiplying a gain factor. The background noise from current time slot is estimated based on comparison result so as to provide estimate value of background noise in the current time window. Independent claims are included for the following: (1) device for estimating background noise; and (2) hearing device.

Description

Die vorliegende Erfindung betrifft ein Verfahren zum Schätzen eines Störgeräusches durch Bereitstellen eines Werts für die Leistungsdichte eines Gesamtsignals, das ein Nutzsignal und das zu schätzende Störgeräusch enthält, in einem aktuellen Zeitfenster, Vergleichen des Werts des Gesamtsignals mit einem mit einem Verstärkungsfaktor multiplizierten Schätzwert eines Störgeräusches aus einem dem aktuellen Zeitfenster vorausgehenden Zeitfenster und Verwenden des kleineren der beiden Werte des Vergleichs als Vorschätzwert für das Störgeräusch in dem aktuellen Zeitfenster. Darüber hinaus betrifft die vorliegende Erfindung eine Vorrichtung zum Schätzen eines Störgeräusches einer Eingangseinrichtung zum Bereitstellen des Werts für die Leistungsdichte des Gesamtsignals und einer rekursiven Minimumschätzeinrichtung zum Vergleichen des Werts des Gesamtsignals mit dem Schätzwert des vorausgehenden Zeitfensters. Des Weiteren betrifft die vorliegende Erfindung auch eine Hörvorrichtung mit einer solchen Vorrichtung zum Schätzen eines Störgeräusches. Unter einer Hörvorrichtung wird hier jedes im oder am Ohr tragbare, schallausgebende Gerät, insbesondere ein Hörgerät, ein Headset, Köpfhörer und dergleichen verstanden.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. Moreover, 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. Furthermore, 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.

Hörgeräte sind tragbare Hörvorrichtungen, die zur Versorgung von Schwerhörenden dienen. Um den zahlreichen individuellen Bedürfnissen entgegenzukommen, werden unterschiedliche Bauformen von Hörgeräten wie Hinter-dem-Ohr-Hörgeräte (HdO), Hörgerät mit externem Hörer (RIC: receiver in the canal) und In-dem-Ohr-Hörgeräte (IdO), z.B. auch Concha-Hörgeräte oder Kanal-Hörgeräte (ITE, CIC), bereitgestellt. Die beispielhaft aufgeführten Hörgeräte werden am Außenohr oder im Gehörgang getragen. Darüber hinaus stehen auf dem Markt aber auch Knochenleitungshörhilfen, implantierbare oder vibrotaktile Hörhilfen zur Verfügung. Dabei erfolgt die Stimulation des geschädigten Gehörs entweder mechanisch oder elektrisch. Hörgeräte besitzen prinzipiell als wesentliche Komponenten einen Eingangswandler, einen Verstärker und einen Ausgangswandler. Der Eingangswandler ist in der Regel ein Schallempfänger, z. B. ein Mikrofon, und/oder ein elektromagnetischer Empfänger, z. B. eine Induktionsspule. Der Ausgangswandler ist meist als elektroakustischer Wandler, z. B. Miniaturlautsprecher, oder als elektromechanischer Wandler, z. B. Knochenleitungshörer, realisiert. Der Verstärker ist üblicherweise in eine Signalverarbeitungseinheit integriert. Dieser prinzipielle Aufbau ist in FIG 1 am Beispiel eines Hinter-dem-Ohr-Hörgeräts dargestellt. In ein Hörgerätegehäuse 1 zum Tragen hinter dem Ohr sind ein oder mehrere Mikrofone 2 zur Aufnahme des Schalls aus der Umgebung eingebaut. Eine Signalverarbeitungseinheit 3, die ebenfalls in das Hörgerätegehäuse 1 integriert ist, verarbeitet die Mikrofonsignale und verstärkt sie. Das Ausgangssignal der Signalverarbeitungseinheit 3 wird an einen Lautsprecher bzw. Hörer 4 übertragen, der ein akustisches Signal ausgibt. Der Schall wird gegebenenfalls über einen Schallschlauch, der mit einer Otoplastik im Gehörgang fixiert ist, zum Trommelfell des Geräteträgers übertragen. Die Energieversorgung des Hörgeräts und insbesondere die der Signalverarbeitungseinheit 3 erfolgt durch eine ebenfalls ins Hörgerätegehäuse 1 integrierte Batterie 5.Hearing aids are portable hearing aids that are used to care for the hearing impaired. To meet the numerous individual needs, 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). The hearing aids listed by way of example are worn on the outer ear or in the ear canal. In addition, 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

Bei vielen Anwendungen, insbesondere bei Hörgeräten und Mobiltelefonen, ist das Nutzsignal, bei dem es sich meist um Sprache handelt, oft durch Störgeräusche gestört. Während stationäre Störgeräusche in der Regel für Sprachverbesserungssysteme bekannter Art kein größeres Problem darstellen, sind nicht stationäre Störgeräusche meist eine größere Herausforderung. Besonders betroffen sind einkanalige (d. h. es wird ein einziges Mikrofon benutzt), modellbasierte Sprachverbesserungssysteme, die auch sehr instationäre Störgeräusche unterdrücken sollen. Derartige einkanalige Sprachverbesserungssysteme können den Hörer entlasten, indem sie Störgeräusche entsprechend dämpfen.In many applications, especially in hearing aids and mobile phones, the useful signal, which is usually language, is often disturbed by noise. While 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.

Einkanalige Störgeräuschereduktion wird typischerweise durch so genannte "Wiener-Filter" durchgeführt. Beim Erstellen eines Wiener-Filters ist es notwendig, zumindest die spektrale Störleistungsdichte (PSD) zu schätzen. Konventionelle Sprachverbesserungssysteme setzen üblicherweise voraus, dass die Störgeräusche eher stationär sind, d. h. die Charakteristik des Störgeräuschs ändert sich nur langsam in Abhängigkeit von der Zeit. So können die Störgeräuschcharakteristiken während Sprachpausen geschätzt werden, was jedoch eine robuste Sprachaktivitätsdetektion (VAD) erfordert.Single-channel noise reduction is typically performed by so-called "Wiener filters". When creating a Wiener filter, it is necessary to estimate at least the spectral noise power density (PSD). 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).

Weiterentwickelte Verfahren arbeiten nach dem Prinzip der "Minimum-Statistik" bzw. des "Minimum Tracking". Sie sind in der Lage, die Störgeräuschschätzung auch während einer Sprachaktivität zu aktualisieren und benötigen somit keine VAD. Bei dem Minimum-Statistik-Verfahren wird verrauschte Sprache in Unterbänder zerlegt, und es wird in einem bestimmten Zeitintervall nach Minima in diesen Unterbändern gesucht. Wegen der hohen Dynamik des Sprachsignals sollten die Minima der spektralen Rauschleistungsdichte entsprechen, wenn das Rauschen beziehungsweise das Störgeräusch hinreichend stationär ist. Die Minima werden als Eingangsgrößen für die Einstellung eines Verstärkungsfaktor im jeweiligen Frequenzband verwendet. Das Verfahren scheitert jedoch, wenn das Störgeräusch einen gewissen Grad an Unstationarität überschreitet. Dies bedeutet, dass seine Leistungsfähigkeit in sehr instationären Umgebungen (z. B. Geplapper in einer Cafeteria) zusammenbricht. Hinsichtlich der Störgeräuschreduktion mit so genanntem "Recursive Minimum Tracking" bzw. "Minimum Statistik" wird auf das Buch von Eberhard Hänsler und Gerhard Schmidt: "Acoustic Echo and Noise Control: Appractial Approach", Wiley-Interscience-Verlag, 2004 und auf den Artikel von R. Martin: "Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum Statistics", IEEE Transactions on Speech and Audio Processing, 2001, 9 (5), Seiten 504 bis 512 verwiesen.Further developed methods work on the principle of "minimum statistics" or "minimum tracking". They are able to update the noise estimate even during voice activity and thus do not require VAD. In the minimum statistics method, 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. However, 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). With regard to the noise reduction with so-called "Recursive Minimum Tracking" or "Minimum Statistics" is on the book of Eberhard Hänsler and Gerhard Schmidt: "Acoustic Echo and Noise Control: Appraisal Approach", Wiley-Interscience-Verlag, 2004 and on the article of R. Martin: "Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum Statistics", IEEE Transactions on Speech and Audio Processing, 2001, 9 (5), pages 504-512 directed.

In jüngster Zeit wurden so genannte "Codebuch-basierte" Sprachverbesserungstechniken entwickelt. Diese nutzen ein Vorabwissen über Sprache und Störgeräusch. Die Hauptidee besteht darin, die spektralen Einhüllenden und die Breitbandsignalleistungen (Verstärkungsfaktoren) von Sprache und Störgeräusch aus dem gestörten Signal zu schätzen. Typische spektrale Einhüllende von Sprache und unterschiedlichen Störgeräuschklassen werden in Codebüchern gespeichert. Für die Schätzung wird zunächst ein Paar (ein Spracheintrag und ein Störgeräuscheintrag) von spektralen Einhüllenden aus den entsprechenden Codebüchern genommen. Die optimalen Verstärkungsfaktoren (d. h. die breitbandige Sprachleistung und die breitbandige Störgeräuschleistung) werden durch Maximieren eines gewissen Optimierungskriteriums geschätzt. Als Kriterium gilt beispielsweise, dass die Summe der Sprach- und Störgeräusch-Codebuch-Einträge dem aktuellen gestörten Signal soweit wie möglich entspricht. In einem zweiten Schritt wird entweder das Paar (zusammen mit den zugehörigen, geschätzten Verstärkungsfaktoren), das mit höchster Wahrscheinlichkeit dem aktuellen gestörten Spektrum entspricht, ausgewählt, oder es wird jedes Paar mit der Wahrscheinlichkeit gewichtet, dass es dem aktuellen gestörten Schallspektrum entspricht, und alle so gewichteten Paare werden aufsummiert. Hierdurch werden Schätzwerte für die Sprach- und Störgeräuschkomponenten des gestörten Schallspektrums erhalten. Diese Schätzwerte werden als Eingangsgrößen für eine nachfolgende Störgeräuschereduktion, beispielsweise durch einen "Wiener-Filter", verwendet. Dieses Schätzverfahren wird in kurzen Zeitfenstern (z. B. 8 ms) durchgeführt, sodass schnellen Änderungen der Störgeräuschcharakteristik nahezu unverzögert gefolgt werden kann. Ein Minimum-Statistik-Schätzer kann derartigen Änderungen nur mit einer Verzögerung im Bereich von einigen wenigen Sekunden folgen.Recently, so-called "codebook-based" speech enhancement techniques have been developed. These 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. For the estimation, first, a pair (a speech entry and a noise input) of spectral envelopes are taken from the respective codebooks. The optimal gain factors (i.e., the wideband speech power and the wideband noise power) are estimated by maximizing a certain optimization criterion. By way of example, the criterion is that the sum of the speech and noise codebook entries corresponds as far as possible to the current disturbed signal. In 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.

Ein derartiger Codebuch-basierter Algorithmus ist aus dem Artikel von T. Rosenkranz, "Noise Codebook Adaptation for Codebook-Based Noise Reduction, in Proceedings of International Workshop on Acoustic Echo and Noise Control (IWAENC), Tel Aviv, August 2010 bekannt.Such a codebook-based algorithm is known from the article of T. Rosenkranz, "Noise Codebook Adaptation for Codebook-Based Noise Reduction, in Proceedings of International Workshop on Acoustic Echo and Noise Control (IWAENC), Tel Aviv, August 2010 known.

Es bestehen jedoch auch drei gravierende Nachteile bei dem Codebuch-basierten Ansatz. Zum Ersten ist die Störgeräuschschätzung auf einen vordefinierten Satz an Codebucheinträgen beschränkt. Da diese Einträge spektrale Einhüllende repräsentieren, sind sie entlang der Frequenzachse geglättet. Dies bedeutet, dass beispielsweise scharfe spektrale Spitzen nicht modelliert sind. Zum zweiten bedeutet die Fähigkeit des Codebuch-basierten Ansatzes, unverzögert auf Störgeräuschänderungen zu reagieren, dass die Schätzung stark schwankt. Da die Schätzung des Breitbandpegels naturgemäß nicht perfekt ist und deshalb relativ stark um den wahren Wert schwankt, kommt es zu unangenehmen Artefakten im störgeräusch-befreiten Signal. Zum Dritten kann dieser Codebuch-basierte Ansatz keine Geräuschklassen handhaben, die nicht trainiert wurden.However, there are also three serious drawbacks to the codebook-based approach. First, 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. Second, 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. Third, this codebook-based approach can not handle noise classes that have not been trained.

Aus dem Dokument EP 2 109 329 A2 ist ein mehrstufiges Schätzverfahren zur Störgeräuschreduktion bekannt. Ebenso ist ein entsprechendes Hörgerät beschrieben. Es werden zwei Schätzalogrithmen verwendet, wobei der eine zum Parametrieren des anderen dient. Ferner kann eine der Schätzalgorithmen eine rekursive Glättung enthalten.From the document EP 2 109 329 A2 is a multi-stage estimation method for noise reduction known. Likewise, a corresponding hearing aid is described. Two estimation algorithms are used, one for parameterizing the other. Further, one of the estimation algorithms may include recursive smoothing.

Die Aufgabe der vorliegenden Erfindung besteht somit darin, ein Verfahren und eine Vorrichtung vorzuschlagen, mit denen es möglich ist, auch unbekannte Störgeräusche möglichst rasch schätzen zu können.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.

Erfindungsgemäß wird diese Aufgabe gelöst durch ein Verfahren zum Schätzen eines Störgeräusches durch

  • Bereitstellen eines Werts für die Leistungsdichte eines Gesamtsignals, das ein Nutzsignal und das zu schätzende Störgeräusch enthält, in einem aktuellen Zeitfenster,
  • Vergleichen des Werts des Gesamtsignals mit einem mit einem Verstärkungsfaktor multiplizierten Schätzwert eines Störgeräusches aus einem dem aktuellen Zeitfenster vorausgehenden Zeitfenster und
  • Ermitteln und Verwenden des kleineren der beiden Werte des Vergleichs als Vorschätzwert für das Störgeräusch in dem aktuellen Zeitfenster, sowie
  • Bereitstellen eines Codebuchschätzwerts für das Störgeräusch in dem aktuellen Zeitfenster und
  • Verwenden des größeren Werts von dem Vorschätzwert und dem Codebuchschätzwert als Schätzwert für das Störgeräusch in dem aktuellen Zeitfenster.
According to the invention this object is achieved by a method for estimating a noise by
  • Providing a value for the power density of a total signal containing a useful signal and the noise to be estimated, in a current time window,
  • Comparing the value of the total signal with a gain multiplied by a gain factor Noise from a time window preceding the current time window and
  • Determining and using the smaller of the two values of the comparison as a prediction value for the noise in the current time window, as well
  • Providing a codebook estimate for the noise in the current time window and
  • Using the larger value of the prediction value and the codebook estimate as an estimate of the noise in the current time window.

Darüber hinaus wird erfindungsgemäß bereitgestellt eine Vorrichtung zum Schätzen eines Störgeräusches mit

  • einer Eingangseinrichtung zum Bereitstellen eines Werts für die Leistungsdichte eines Gesamtsignals, das ein Nutzsignal und das zu schätzende Störgeräusch enthält, in einem aktuellen Zeitfenster,
  • einer rekursiven Minimumschätzeinrichtung zum Vergleichen des Werts des Gesamtsignals mit einem mit einem Verstärkungsfaktor multiplizierten Schätzwert eines Störgeräusches aus einem dem aktuellen Zeitfenster vorausgehenden Zeitfenster und zum Ausgeben des kleineren der beiden Werte des Vergleichs als Vorschätzwert für das Störgeräusch in dem aktuellen Zeitfenster, sowie mit
  • einer Codebuchschätzeinrichtung zum Bereitstellen eines Codebuchschätzwerts für das Störgeräusch in dem aktuellen Zeitfenster und
  • einer Logikeinrichtung zum Ermitteln des größeren Werts von dem Vorschätzwert und dem Codebuchschätzwert als Schätzwert für das Störgeräusch in dem aktuellen Zeitfenster.
In addition, the invention provides a device for estimating a noise with
  • an input device for providing a value for the power density of a total signal, which contains a useful signal and the noise to be estimated, in a current time window,
  • a recursive minimum estimator for comparing the value of the overall signal with a gain multiplied estimate of noise from a time window preceding the current time window and outputting the smaller of the two values of the comparison as the noise estimate in the current time window, and
  • a codebook estimator for providing a codebook estimate for the noise in the current time slot and
  • a logic means for determining the larger value of the prediction value and the codebook estimate value as an estimate of the noise in the current time window.

In vorteilhafter Weise wird also erfindungsgemäß das "rekursive Minimumtracking" mit der "Codebuch-basierten Störgeräuschschätzung" kombiniert, um eine verbesserte Reduktion nicht stationärer Störgeräusche zu erreichen. Dadurch werden die oben genannten Nachteile der rekursiven Minimumsuche als auch die Nachteile der Codebuch-basierten Schätzung für sich genommen im Wesentlichen eliminiert.Advantageously, according to the invention, the "recursive minimum tracking" is combined with the "codebook-based noise estimation" in order to achieve an improved reduction of non-stationary noise. As a result, the above-mentioned disadvantages of the recursive minimum search as The disadvantages of the codebook-based estimation taken by itself are essentially eliminated.

Vorzugsweise handelt es sich bei dem Wert des Gesamtsignals und dem Schätzwert für ein Störgeräusch jeweils um spektrale Werte. Die Signalverarbeitung in dem erfindungsgemäßen Verfahren wird dann im Spektralbereich durchgeführt.Preferably, 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.

Insbesondere ist es günstig, wenn das Verfahren in mehreren Frequenzkanälen parallel angewendet wird. Das Eingangssignal wird hierzu günstiger Weise in einer Filterbank in die einzelnen Spektralanteile zerlegt.In particular, it is favorable if the method is used in parallel in a plurality of frequency channels. For this purpose, the input signal is advantageously decomposed in a filter bank into the individual spectral components.

Ferner ist es von Vorteil, wenn der Schätzwert für das Störgeräusch in dem aktuellen Zeitfenster mit dem Schätzwert aus dem vorausgehenden Zeitfenster geglättet wird. Dies ist insofern günstig, als dann keine übermäßigen Sprünge bei der Geräuschreduktion auftreten.Furthermore, it is advantageous if 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.

Besonders vorteilhaft ist auch, wenn der Codebuchschätzwert auf Null gesetzt werden kann. Äquivalent hierzu ist, wenn die Codebuchschätzeinrichtung abgeschaltet wird. Dadurch wird der gesamte Algorithmus unempfindlicher gegenüber der Tatsache, ob das Störgeräusch bekannt ist oder nicht.It is also particularly advantageous if 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.

In einer vorteilhaften Anwendung wird das oben geschilderte Verfahren zum Schätzen eines Störgeräuschs für das Reduzieren von Störgeräuschen verwendet. Hier wiederum ist es von besonderem Vorteil, wenn ein derartiges Verfahren zum Reduzieren von Störgeräuschen zum Betrieb eines Hörgeräts genutzt wird beziehungsweise in einem Hörgerät implementiert wird. Dadurch können insbesondere Hörgeräteträger von dem verbesserten, kombinierten Störgeräuschreduktionsverfahren profitieren.In an advantageous application, the above-described method of estimating noise is used for reducing noise. Here, in turn, it is of particular advantage if such a method for reducing noise is used for operating a hearing device or is implemented in a hearing device. As a result, in particular hearing aid wearers can benefit from the improved, combined noise reduction method.

Die oben genannte Vorrichtung zum Schätzen eines Störgeräuschs kann in eine Hörvorrichtung integriert werden. Speziell kann diese Hörvorrichtung als Hörgerät ausgebildet sein.The above-mentioned noise-estimating apparatus may be integrated into a hearing apparatus. specially This hearing device can be designed as a hearing aid.

Die vorliegende Erfindung wird nun anhand der beigefügten Zeichnungen näher erläutert, in denen zeigen:

FIG 1
eine Prinzipskizze eines Hörgeräts gemäß dem Stand der Technik;
FIG 2
ein Schaltbild einer Signalverarbeitung in einem Hörgerät;
FIG 3
ein Schaltbild eines rekursiven Störgeräuschschätzers gemäß dem Stand der Technik;
FIG 4
ein Schaltbild eines kombinierten Störgeräuschschätzers gemäß der vorliegenden Erfindung und
FIG 5
Signalverläufe von Störgeräuschen und Störgeräuschschätzungen nach unterschiedlichen Algorithmen.
The present invention will now be explained in more detail with reference to the accompanying drawings, in which:
FIG. 1
a schematic diagram of a hearing aid according to the prior art;
FIG. 2
a circuit diagram of a signal processing in a hearing aid;
FIG. 3
a circuit diagram of a recursive noise estimator according to the prior art;
FIG. 4
a circuit diagram of a combined noise estimator according to the present invention and
FIG. 5
Waveforms of noise and noise estimates according to different algorithms.

Die nachfolgend näher geschilderten Ausführungsbeispiele stellen bevorzugte Ausführungsformen der vorliegenden Erfindung dar.The embodiments described in more detail below represent preferred embodiments of the present invention.

In einem beispielhaften Hörgerät findet eine Signalverarbeitung gemäß der Skizze von FIG 2 statt. Ein Mikrofon 10 des Hörgeräts liefert ein verrauschtes beziehungsweise gestörtes Signal x(k). Dieses Signal wird mithilfe einer Filterbank 11 spektral in einzelne Frequenzbänder zerlegt. Damit steht ein spektrales Signal X (ejΩ) bereit. Dieses spektrale Signal wird einer Geräuschschätzeinheit 12 zugeführt, die daraus einen Schätzwert Ŝ nn (ejΩ) für die Geräuschleistungsdichte gewinnt. Ein Geräuschreduktionsfilter 13 ermittelt daraus spektrale Gewichte Ĝ (ejΩ) In einem Multiplizierer 14 werden dann die Gewichte Ĝ (ejΩ) mit dem Spektrum X (ejΩ) des Gesamtsignals multipliziert, woraus ein Schätzwert Ŝ (ejΩ) für das Nutzsignal (z. B. reines Sprachsignal) entsteht. Durch eine inverse Filterbank 15 entsteht eine Schätzung ŝ (k) des Nutzsignals im Zeitbereich.In an exemplary hearing aid, signal processing according to the sketch of FIG FIG. 2 instead of. 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 jΩ ) ready. This spectral signal is supplied to a noise estimation unit 12, which obtains therefrom an estimated value Ŝ nn (e jΩ ) for the noise power density . A noise reduction filter 13 determines therefrom spectral weights Ĝ (e jΩ ) In a multiplier 14, the weights Ĝ (e jΩ ) are then multiplied by the spectrum X (e jΩ ) of the total signal, resulting in an estimated value Ŝ (e jΩ ) for the useful signal (e.g. B. pure speech signal) arises. An inverse filter bank 15 produces an estimate ŝ (k) of the useful signal in the time domain.

Erfindungsgemäß wird nun die Geräuschschätzung in der Geräuschschätzungseinheit 12 optimiert. Erfindungsgemäß wird hierzu ein Geräuschschätzalgorithmus basierend auf rekursiver Minimumstatistik und ein Algorithmus basierend auf einem oder mehreren Codebüchern kombiniert. Es entsteht somit ein Geräuschschätzverfahren, welches die entsprechenden Vorteile kombiniert. Beispielsweise wird ein Codebuch-basierter Algorithmus verwendet, wie er in dem eingangs geschilderten Artikel von T. Rosenkranz beschrieben ist. Die Störgeräuschschätzung des Codebuch-basierten Algorithmus wird in den rekursiven Schätzalgorithmus basierend auf der Minimumstatistik ähnlich dem eingangs erwähnten Algorithmus von Eberhard Hänsler und Gerhard Schmidt integriert.According to the invention, the noise estimation in the noise estimation unit 12 is now optimized. According to the invention, a noise estimation algorithm based on recursive minimum statistics and an algorithm based on one or more codebooks are combined for this purpose. This results in a noise estimation method, which has the corresponding advantages combined. For example, 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.

Zum besseren Verständnis der Erfindung wird nachfolgend anhand von FIG 3 ein Modell eines rekursiven Störgeräuschschätzers dargelegt. Das dort dargestellte Verfahren findet in mehreren Frequenz(unter)bändern unabhängig voneinander statt. Die einzelnen Frequenzbänder werden beispielsweise mit der in FIG 2 dargestellten Filterbank 11 gewonnen. Bei dem Eingangssignal X (ejΩ) beziehungsweise |X|2 handelt es sich beispielsweise um ein Periodogramm verrauschter Sprache. Das Ausgangssignal Ŝ nn (ejΩ) entspricht einer Schätzung des Störgeräuschleistungsspektrums. Das Eingangssignals wird in einer Glättungseinheit 16 geglättet. Das geglättete Eingangsspektrum wird in einem Vergleicher 17 mit dem geschätzten Störgeräuschspektrum eines vorhergehenden Fensters verglichen. Hierzu wird das geschätzte Störleistungsspektrum des vorhergehenden Zeitfensters vorab mit einer konstanten "Störgeräuschschätzverstärkung" multipliziert, die dem Wert 1 + ε entspricht, wobei ε < < 1 ist. Für diese Multiplikation ist der Verstärker 18 vorgesehen. Er erhält sein Eingangssignal von einem Verzögerungselement 19, welches seinerseits von dem Störschätzwert Ŝ nn (ejΩ) des aktuellen Zeitfenster gespeist wird. Zur Glättung des Ausgangssignals wird der Schätzwert des vorhergehenden Zeitfensters (Signal nach der Verzögerungseinheit 19) von dem Ausgangssignal des Vergleichers 17 in einem Subtrahierer 20 subtrahiert. Das Differenzsignal wird in einem weiteren Verstärker 21 mit einer Konstante multipliziert. Das resultierende Signal wird schließlich in einem Addierer 22 mit dem Schätzwert des vorhergehenden Zeitfensters addiert, woraus schließlich der geglättete Schätzwert Ŝ nn (ejΩ) resultiert.For a better understanding of the invention is described below with reference to 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. For the input signal X (e jΩ ) or | X | 2 is, for example, a periodogram of noisy speech. The output signal Ŝ nn (e jΩ ) 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. For this purpose, 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 + ε, where ε <<1. For this multiplication, the amplifier 18 is provided. It receives its input signal from a delay element 19, which in turn is fed by the interference estimate Ŝ nn (e jΩ ) of the current time window. For smoothing the output signal, 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 Ŝ nn (e jΩ ).

Mit den Elementen 19, 20, 21 und 22 wird somit eine IIR-Glättung (Infinite Impulse Response) erster Ordnung des geschätzten Störgeräuschspektrums durchgeführt.With the elements 19, 20, 21 and 22, a first order IIR (Infinite Impulse Response) smoothing of the estimated noise spectrum is thus performed.

In dem Vergleicher 17 wird das Minimum der beiden Signale (im aktuellen Zeitfenster und im vorhergehenden Zeitfenster) verwendet. Es handelt sich somit um eine Art effiziente Implementierung des Minimum-Statistik-Algorithmus entsprechend dem Artikel von R. Martin.In the comparator 17, 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.

Das Verhalten dieses bekannten Schätzers geht aus der Grafik von FIG 5 hervor. Die Kurve 23 zeigt das tatsächlich vorliegende Störgeräusch. Beispielsweise handelt es sich um Straßenlärm mit schnell vorbeifahrenden Autos. Die Schätzwerte werden aus einer Mischung dieses Störgeräuschs mit einem Sprachsignal bei einem Abstand (SNR) von 0 dB ermittelt. Die Kurve 24 zeigt die Schätzung des rekursiven Minimum-Tracking-Algorithmus. Wie beispielsweise aus den ersten beiden Sekunden der Schätzung gesehen werden kann, kann der Schätzer dem raschen Anstieg des Störgeräuschs nicht folgen. Der Anstieg des Schätzers wird durch die Konstante ε limitiert. Diese Konstante ε muss klein sein, denn anderenfalls würde die Schätzung dem verrauschten Eingangsspektrum zu rasch folgen und Sprachanteile werden fälschlicherweise in die Störgeräuschschätzung aufgenommen.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 ε. This constant ε must be small, otherwise the estimate would follow the noisy input spectrum too quickly and speech components are erroneously included in the noise estimate.

Erfindungsgemäß erfolgt nun entsprechend dem Beispiel von FIG 4 eine Verbesserung der Störgeräuschschätzung dadurch, dass die rekursive Schätzung mit einer Codebuch-basierten Schätzung kombiniert wird, wobei der kombinierte Algorithmus in der Lage ist, schnellen Störgeräuschfluktuationen rasch zu folgen. Das ist FIG 4 dargestellte Signalflussdiagramm entspricht im Wesentlichen dem von FIG 3. Daher wird auf die Beschreibung von FIG 3 Bezug genommen. Eine Codebuch-basierte Störgeräuschschätzung wird mithilfe einer Maximum-Operation in einer zweiten Vergleichereinheit 27 (Logikeinrichtung) unmittelbar hinter der Vergleichereinheit 17 mit der Minimum-Operation in die Schätzvorrichtung integriert. Die Vergleichseinheit 27 erhält eine Codebuch-Schätzung Ŝ nnCB von einer in FIG 4 nicht näher dargestellten CodebuchSchätzeinrichtung. Wenn folglich das tatsächliche Störgeräusch deutlich unterschätzt wird (der Schätzwert des rekursiven Minimum-Tracking-Algorithmus liegt unterhalb demjenigen des Codebuch-basierten Algorithmus), so wird der Codebuch-basierte Schätzwert genommen. Der rekursive Teil des Algorithmus ist dann in der Lage, dem Störgeräusch von einem höheren Pegel aus nachzugehen. Der erfindungsgemäße, kombinierte Algorithmus kann daher auf Änderungen des Störgeräuschpegels genauso schnell reagieren wie Codebuch-Schätzungen.According to the invention is carried out according to the example of FIG. 4 improving the noise estimate by combining the recursive estimate with a codebook based estimate, the combined algorithm being able to quickly track fast noise fluctuations. This is 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 Ŝ nnCB from an in FIG. 4 not shown codebook estimation device. Thus, if the actual noise is significantly underestimated (the estimate of the recursive minimum tracking algorithm is below that of the codebook based algorithm), then 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 zeigt dieses Verhalten der kombinierten Schätzung. Die Codebuchschätzung ist mit Kurve 25 dargestellt. Die Schätzung des kombinierten Algorithmus ist mit Kurve 26 wiedergegeben. Verglichen mit der Codebuchschätzung 25 folgt die kombinierte Schätzung 26 der Erhöhung des Störgeräuschpegels mit einer sehr geringen Verzögerung, die auf den Glättungsteil 20, 21, 22 des Algorithmus zurückzuführen ist. Es ist jedoch ersichtlich, dass der kombinierte Algorithmus dem Anstieg des Störgeräuschpegels viel schneller folgt als der rekursive Algorithmus 24 allein. Außerdem kann erkannt werden, dass der erfindungsgemäße, kombinierte Algorithmus eine bessere Schätzung liefert, wenn die Codebuch-basierte Schätzung 25 das tatsächliche Störgeräusch 23 unterschätzt. Im Zeitbereich zwischen 4 und 6 Sekunden nämlich ist die Codebuch-Schätzung 25 deutlich geringer als das tatsächliche Störgeräusch. Da aber der rekursive Teil des Algorithmus dem Störgeräusch folgen kann, liegt die kombinierte Schätzung 26 wesentlich näher an dem realen Störgeräusch als die Codebuch-basierte Schätzung 25 oder die rekursive Schätzung 24 allein. 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. Compared with the codebook estimate 25, 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. However, it can be seen that the combined algorithm tracks the increase in noise level much faster than the recursive algorithm 24 alone. In addition, it can be seen that 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. However, since the recursive portion of the algorithm can track the noise, the combined estimate 26 is much closer to the real noise than the codebook based estimate 25 or the recursive estimate 24 alone.

In vorteilhafter Weise wird also eine Codebuch-basierte Störgeräuschschätzung mit einer rekursiven Störgeräuschschätzung kombiniert. Die Vorteile jeder einzelnen dieser Schätzungen werden dabei für die Kombination gewonnen, während die Nachteile minimiert werden.Advantageously, therefore, 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.

Die Vorteile der Kombination liegen darin, dass der kombinierte Algorithmus schnellen Störgeräuschfluktuationen wesentlich rascher folgen kann als konventionelle rekursive Störgeräuschschätzer. Ein weiterer Vorteil liegt darin, dass durch das Einkoppeln des Codebuch-basierten Schätzalgorithmus auf die vorgeschlagene Art und Weise der Schätzer zu einem konventionellen rekursiven Schätzer wird, wenn die Codebuch-basierte Schätzung abgeschaltet beziehungsweise auf Null gesetzt wird. Dies wiederum verbessert die Robustheit des Algorithmus. Ferner liegt ein Vorteil der vorgeschlagenen Kombination darin, dass der Algorithmus dem Störgeräusch weiter folgen kann, wenn der Codebuch-basierte Algorithmus den tatsächlichen Störgeräuschpegel unterschätzt. Der kombinierte Algorithmus kann daher Bereiche überbrücken, in denen die Codebuch-basierte Schätzung entweder das Störgeräusch unterschätzt oder abgeschaltet ist. Außerdem schwankt die Geräuschschätzung wesentlich weniger als die Codebuch-basierte Schätzung allein, was zu einer wesentlich angenehmeren Schallwiedergabe mit verminderten Artefakten führt. Zudem kann der vorgeschlagene Schätzer Störgeräusche handhaben, für die der Codebuch-basierte Algorithmus nicht trainiert wurde. Dies liegt an dem rekursiven Teil des Algorithmus, der von der Codebuch-basierten Schätzung unabhängig ist.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.

Claims (10)

  1. Method for estimating interference noise by
    - providing a value for the power density of a total signal, containing a wanted signal and the interference noise to be estimated, in a current time window,
    - comparing the value of the total signal with an estimated value, multiplied by an amplification factor (18), of interference noise from a time window preceding the current time window and
    - using the smaller (17) of the two values from the comparison as a preliminary estimated value for the interference noise in the current time window,
    characterised by
    - providing a codebook estimated value for the interference noise in the current time window and
    - determining and using the larger (27) of the preliminary estimated value and the codebook estimated value as the estimated value for the interference noise in the current time window.
  2. Method according to claim 1, wherein the value of the total signal and the estimated value for interference noise are each spectral values.
  3. Method according to claim 1 or 2, wherein the estimated value for the interference noise in the current time window is smoothed with the estimated value from the preceding time window (20, 21, 22).
  4. Method according to one of the preceding claims, wherein the codebook estimated value is set to zero.
  5. Method according to one of the preceding claims that is applied in a plurality of frequency channels in parallel.
  6. Method for reducing interference noise by estimating the interference noise in accordance with one of the preceding claims and reducing the interference noise according to the estimated value.
  7. Method for operating a hearing aid, wherein interference noise is reduced according to claim 6.
  8. Device for estimating interference noise, comprising
    - an input device (11) for providing a value for the power density of a total signal, containing a wanted signal and the interference noise to be estimated, in a current time window,
    - a recursive minimum estimation device (17, 18, 19) for comparing the value of the total signal with an estimated value, multiplied by an amplification factor, of interference noise from a time window preceding the current time window and for outputting the smaller of the two values from the comparison as a preliminary estimated value for the interference noise in the current time window,
    characterised by
    - a codebook estimation device to provide a codebook estimated value for the interference noise in the current time window and
    - a logic device (27) to determine the larger of the preliminary estimated value and the codebook estimated value as the estimated value for the interference noise in the current time window.
  9. Hearing device into which a device according to claim 8 is integrated for the purpose of estimating interference noise.
  10. Hearing device according to claim 9, which is embodied in the form of a hearing aid.
EP12154134.6A 2011-02-17 2012-02-07 Method and device for estimating an interference noise Active EP2495724B1 (en)

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US9763016B2 (en) * 2014-07-31 2017-09-12 Starkey Laboratories, Inc. Automatic directional switching algorithm for hearing aids
DE102015201073A1 (en) 2015-01-22 2016-07-28 Sivantos Pte. Ltd. Method and apparatus for noise suppression based on inter-subband correlation
CN109328380B (en) * 2016-06-13 2023-02-28 Med-El电气医疗器械有限公司 Recursive noise power estimation with noise model adaptation
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CN113008361B (en) * 2021-02-04 2023-08-15 国网湖南省电力有限公司 Method and device for detecting noise of substation boundary of transformer substation against environmental interference

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US8385572B2 (en) * 2007-03-12 2013-02-26 Siemens Audiologische Technik Gmbh Method for reducing noise using trainable models
DE112007003674T5 (en) * 2007-10-02 2010-08-12 Akg Acoustics Gmbh Method and apparatus for single-channel speech enhancement based on a latency-reduced auditory model
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EP2495724A1 (en) 2012-09-05

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