EP1971186A2 - Procédé de réduction de bruits parasites à l'aide de modèles pouvant être entraînés - Google Patents

Procédé de réduction de bruits parasites à l'aide de modèles pouvant être entraînés Download PDF

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Publication number
EP1971186A2
EP1971186A2 EP08102007A EP08102007A EP1971186A2 EP 1971186 A2 EP1971186 A2 EP 1971186A2 EP 08102007 A EP08102007 A EP 08102007A EP 08102007 A EP08102007 A EP 08102007A EP 1971186 A2 EP1971186 A2 EP 1971186A2
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EP
European Patent Office
Prior art keywords
signal
model
models
noise
useful
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.)
Ceased
Application number
EP08102007A
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German (de)
English (en)
Other versions
EP1971186A3 (fr
Inventor
Oliver Dressler
Eghart Fischer
Ulrich Dr. Kornagel
Wolfgang Sörgel
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.)
Sivantos GmbH
Original Assignee
Siemens Audioligische Technik GmbH
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Publication date
Application filed by Siemens Audioligische Technik GmbH filed Critical Siemens Audioligische Technik GmbH
Publication of EP1971186A2 publication Critical patent/EP1971186A2/fr
Publication of EP1971186A3 publication Critical patent/EP1971186A3/fr
Ceased legal-status Critical Current

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    • 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
    • H04R25/505—Customised settings for obtaining desired overall acoustical characteristics using digital signal processing
    • 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
    • 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
    • H04R2225/39—Aspects relating to automatic logging of sound environment parameters and the performance of the hearing aid during use, e.g. histogram logging, or of user selected programs or settings in the hearing aid, e.g. usage logging
    • 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
    • H04R2225/41—Detection or adaptation of hearing aid parameters or programs to listening situation, e.g. pub, forest
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2460/00—Details of hearing devices, i.e. of ear- or headphones covered by H04R1/10 or H04R5/033 but not provided for in any of their subgroups, or of hearing aids covered by H04R25/00 but not provided for in any of its subgroups
    • H04R2460/01—Hearing devices using active noise cancellation

Definitions

  • the present invention relates to a method for reducing noise in hearing devices by receiving an input signal, modeling the input signal with a useful signal model and a Störsignalmodell and reducing the noise component of the input signal using the estimated by the Störsignalmodell Störschalls.
  • hearing device is understood to mean in particular any device that can be worn on the ear, such as a hearing aid, 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) and in-the-ear hearing aids (ITO), e.g. also Concha hearing aids or canal hearing aids (CIC), provided.
  • BTE behind-the-ear hearing aids
  • ITO in-the-ear hearing aids
  • CIC 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 are installed for recording the sound from the environment.
  • 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 of the signal processing unit 3 is carried out by a likewise integrated into the hearing aid housing 1 battery. 5
  • Monaural noise reduction methods are an integral part of hearing aids. For this purpose, procedures in the frequency domain with spectral weighting, z. As the Wiener filter or the spectral subtraction used.
  • the proportion of the noise that is received must be estimated from the received noisy signal.
  • the method of minimum statistics can be used for this estimation.
  • the spectral weighting according to Ephraim-Malah also requires an estimate of the amplitude spectrum of the useful signal.
  • the payload amplitude spectra are assumed to be Gaussian distributed in order to determine the weighting rules according to Ephraim-Malah. (see. EPHRAIM, Y .; MALAH, D .: Speech Enhancement Using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator. In: IEEE Transactions on Acoustics, Speech and Signal Processing, Dec. 1984, Vol. ASSP-32 no. 6, pages 1109-1121 ).
  • a method for processing an input signal in a signal processing unit of a hearing device is known.
  • setting parameters of a signal processing unit related to the noise reduction are set depending on the result of signal analysis of the input signal. If interference signals are detected, they are assigned to different interference signal categories. Depending on the detected noise category, different algorithms for noise reduction on and off.
  • the object of the present invention is thus to improve the effect of noise reduction methods.
  • a method for reducing noise in hearing devices by recording an input signal, modeling the input signal with a useful signal model and a Störsignalmodell and reducing the noise component of the input signal using the Störsignalmodells and / or the Nutzsignalmodells and detecting a signal statistics of the Input signal and changing the Nutzsignalmodells and / or the Störsignalmodells depending on the signal statistics.
  • the term "change” here does not mean a "replacement" of a model, but the content modification and adaptation of a model.
  • the knowledge is used that apriori existing information for the detection of the signal statistics for obtaining the parameters of suitable models of the useful signal and the interference signal can be used.
  • the fixed model parameters with statistically relevant training data must be set in such a way that the most comprehensive possible mapping of the signal statistics is achieved.
  • the noise reduction according to the invention is thus not operated as in known methods with fixed assumptions to the signal estimate or with pre-trained parameters of signal models.
  • the models of noise reduction can be optimally adapted to the current situation of the hearing device wearer or user of the hearing device.
  • one or both of the useful signal model and the interference signal model of the invention Noise Reduction Algorithm autoregressive models with trained codebooks, models with over-complete codebooks, models based on transforms or wavelet representations, models with decompositions into tonal, transient and noise-like components, and signal-statistical modeling.
  • the models to be trained can be started with "advance knowledge”.
  • a data logging of the input signal and / or its signal statistics, which relates to model parameters of the model to be changed, is performed and the model to be changed is trained with the aid of the recorded data.
  • the recorded data can be used to train in real time.
  • the data logging and the training is carried out continuously automatically.
  • a currently newly trained signal model is always available.
  • At least one further model selectable by the user of the hearing device can be trained and used instead of the useful signal model or the interference signal model for noise reduction. This allows the user to intervene in the decision-making process about the model to be used and subjectively influence the noise reduction.
  • the model to be changed can also be changed on the basis of a real-time estimation of an interference signal or useful signal.
  • model parameters can also be obtained by estimates.
  • a further preferred embodiment of the present invention consists in that at least one further model is used in addition to the interference signal model and the useful signal model for the estimation of the interference noise or useful sound.
  • the use of multiple parallel Störsignalmodelle also complex, resulting from several different sources disturbances can be effectively suppressed.
  • the noise suppression systems presented here relate to systems in which at least one noisy input signal is modeled by modeling.
  • at least one model for a useful signal component and an interference signal component are used, the parameters of which are estimated as a function of the input signal such that the model optimally describes the input signal according to a specific criterion.
  • autoregressive models with trained codebooks come as a model here and models with over-complete codebooks, models based on transformations such as Fourier transform, discrete cosine transformations or wavelet representations, models with decomposition into tonal, transient and noise-like components, signal-statistical modeling or other suitable models.
  • a noise suppression can be carried out by means of various known techniques.
  • the system according to FIG. 2 has a model-based noise reduction algorithm 10 as the central building block. It is supplied with an input signal E and delivers a corresponding output signal A.
  • the noise reduction algorithm 10 is based on a useful signal model 11 on the one hand and on a noise signal model 12 on the other hand. It also supplies a data logging unit 13 in which the input signal E is also recorded. Thus, recorded model parameters M, recorded quality measures Q as well as the recorded input signal E can be read out by the datalogging unit 13.
  • the input signal and / or its signal statistics which is mapped by the corresponding model parameters, are recorded in the datalogging unit 13 by means of data logging.
  • the recording can be done constantly or depending on the quality of the currently achieved noise reduction.
  • a corresponding quality measure Q is constantly available and can z. B. falls below a threshold recording start.
  • the recording can also be started manually by the user, for example.
  • the training for improved model parameters M of the useful signal and / or the interfering signal can then take place at the time of the evaluation by the hearing device acoustician.
  • This night training is in FIG. 2 symbolized by the arrow 14.
  • an exchange of the already used signal models by the newly trained models can take place.
  • a further improvement can be achieved by using not only one implementation each for the useful or interference signal model, but in each case several models for different signal statistics.
  • the core in turn is the model-based noise reduction algorithm 20, which is fed with an input signal E and which supplies a corresponding output signal A. It relies on several useful signal models 211, 212 and several Störsignalmodelle 221, 222 and 223.
  • a dedicated model selection unit 24 selects for the noise reduction algorithm 20 each have a model of the Nutzsignalmodellen 211, 212 and the Störsignalmodellen 221, 222 and 223 from.
  • the model selection is based on a situation detection, which performs a situation detection unit 25 on the basis of the input signal E. With the help of the algorithm for situation detection, the most suitable signal model for the current situation is selected.
  • the situation detection is suitable, for example, to select the appropriate useful signal models for speech or music.
  • a datalogging unit 23 which also records signals from the noise reduction unit 20 in addition to the input signal E. Optionally, it also records data about the selected models as indicated by the dashed arrows in FIG. 3 is shown symbolically.
  • the Datalogging unit 23 then provides as in the embodiment of FIG. 2 recorded model parameters M, a recorded quality measure Q and the recorded input signal E.
  • the model parameters M are used to change the useful signal models 211, 212 or the interference signal models 221, 222 and 223.
  • the data provided by the datalogging unit 23 may be used by a hearing care professional to change the in-use static models 211, 212, 221, 222, and 223. That is, the hearing care professional may, for example, change the models with the recorded model parameters M and the recorded quality measure Q, as indicated by the arrow 26 in FIG. 3 is symbolized. During operation, the models are then static again.
  • the models that have been newly trained with the aid of datalogging can then either be added to the existing signal models depending on the available memory space or existing models can be exchanged.
  • the associated poor quality measures Q or their rare use can then be added to the existing signal models depending on the available memory space or existing models can be exchanged.
  • FIG. 2 and FIG. 3 are the Nutzsignal- and Störsignalmodelle during operation static.
  • Dynamic models are also used.
  • the core of this system is in turn the model-based noise reduction algorithm 30, in which the input signal E is fed, and from which a corresponding output signal A with reduced noise can be obtained.
  • the noise algorithm 30 relies here not only on a static payload model 31 and a static noise signal model 32, but also on an updatable or dynamic Nutzsignalmodell 37 and also updatable, dynamic Störsignalmodell 38.
  • Both dynamic models can be trained automatically by a training algorithm 39. This one gains training information from the input signal E and receives additional situation data from the situation detection 35, which is also fed by the input signal E.
  • the model selection unit 34 makes a selection of the models to be used on the basis of predetermined criteria, optionally fed back by the noise reduction algorithm 30.
  • the system shown has the following mode of operation: In principle, it is possible to automatically adapt the signal models 37, 38 to the currently available signal statistics. For this purpose, depending on the detected situation in the situation recognition unit 35, at least one new model adapted to the individual signal statistics for the useful or interference signal is trained. This ongoing training usually provides constantly changing signal models. If the quality measure deteriorates from the model-based noise reduction 30 and if sufficiently stable signal statistics are present in the new adapted signal model, the currently used signal models can be replaced by the newly trained signal models or supplemented by these new signal models.
  • the decision to replace a signal model by a newly trained signal model can also be left to the user.
  • the ongoing training automatically pre-selects the new models, and the user can then switch between two combinations of effective signal models, for example by interaction via a remote control. The better combination for the user in the current situation is then selected.
  • the parameters of the above-described signal models are obtained by a training algorithm.
  • the signal models can also be extended by appropriate model parameters from a real-time estimate. This means that the model parameters instead of training or supplementary to be adjusted to this by estimate.
  • To estimate the interference signal for example, the method of minimum statistics or the residual interference at the output of a directional microphone signal processing can be used.
  • the parameters from the continuous training are provided by the estimated parameters with a hypothesis for the corresponding signal model.

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  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Human Computer Interaction (AREA)
  • Quality & Reliability (AREA)
  • Computational Linguistics (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • General Health & Medical Sciences (AREA)
  • Neurosurgery (AREA)
  • Otolaryngology (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Circuit For Audible Band Transducer (AREA)
  • Noise Elimination (AREA)
EP08102007.5A 2007-03-12 2008-02-26 Procédé de réduction de bruits parasites à l'aide de modèles pouvant être entraînés Ceased EP1971186A3 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE102007011808A DE102007011808A1 (de) 2007-03-12 2007-03-12 Verfahren zur Reduzierung von Störgeräuschen mit trainierbaren Modellen

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EP1971186A2 true EP1971186A2 (fr) 2008-09-17
EP1971186A3 EP1971186A3 (fr) 2016-07-20

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EP08102007.5A Ceased EP1971186A3 (fr) 2007-03-12 2008-02-26 Procédé de réduction de bruits parasites à l'aide de modèles pouvant être entraînés

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EP (1) EP1971186A3 (fr)
AU (1) AU2008201143B2 (fr)
DE (1) DE102007011808A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10114101A1 (de) 2001-03-22 2002-06-06 Siemens Audiologische Technik Verfahren zum Verarbeiten eines Eingangssignals in einer Signalverarbeitungseinheit eines Hörgerätes sowie Schaltung zur Durchführung des Verfahrens
US20040190739A1 (en) * 2003-03-25 2004-09-30 Herbert Bachler Method to log data in a hearing device as well as a hearing device

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2001076321A1 (fr) * 2000-04-04 2001-10-11 Gn Resound A/S Prothese auditive a classification automatique de l'environnement d'ecoute
DK1359787T3 (en) * 2002-04-25 2015-04-20 Gn Resound As Fitting method and hearing prosthesis which is based on signal to noise ratio loss of data
WO2006114101A1 (fr) * 2005-04-26 2006-11-02 Aalborg Universitet Detection de parole presente dans un signal bruyant et amelioration de parole a l'aide de cette detection
DK1760696T3 (en) * 2005-09-03 2016-05-02 Gn Resound As Method and apparatus for improved estimation of non-stationary noise to highlight speech

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10114101A1 (de) 2001-03-22 2002-06-06 Siemens Audiologische Technik Verfahren zum Verarbeiten eines Eingangssignals in einer Signalverarbeitungseinheit eines Hörgerätes sowie Schaltung zur Durchführung des Verfahrens
US20040190739A1 (en) * 2003-03-25 2004-09-30 Herbert Bachler Method to log data in a hearing device as well as a hearing device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
EPHRAIM, Y.;; MALAH, D.: "Speech Enhancement Using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator", IEEE TRANSACTIONS ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, vol. 32, no. 6, December 1984 (1984-12-01), pages 1109 - 1121

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DE102007011808A1 (de) 2008-09-18
AU2008201143A1 (en) 2008-10-02
EP1971186A3 (fr) 2016-07-20
AU2008201143B2 (en) 2010-06-24

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