EP0712262A1 - Prothèse auditive - Google Patents

Prothèse auditive Download PDF

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Publication number
EP0712262A1
EP0712262A1 EP94117796A EP94117796A EP0712262A1 EP 0712262 A1 EP0712262 A1 EP 0712262A1 EP 94117796 A EP94117796 A EP 94117796A EP 94117796 A EP94117796 A EP 94117796A EP 0712262 A1 EP0712262 A1 EP 0712262A1
Authority
EP
European Patent Office
Prior art keywords
hearing aid
signals
aid according
principle
neural
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.)
Withdrawn
Application number
EP94117796A
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German (de)
English (en)
Inventor
Oliver Dipl.-Ing. Weinfurtner
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
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Audioligische Technik GmbH filed Critical Siemens Audioligische Technik GmbH
Priority to EP94117796A priority Critical patent/EP0712262A1/fr
Publication of EP0712262A1 publication Critical patent/EP0712262A1/fr
Withdrawn legal-status Critical Current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
    • H04R25/507—Customised settings for obtaining desired overall acoustical characteristics using digital signal processing implemented by neural network or fuzzy logic
    • 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

Definitions

  • the invention relates to a hearing aid with an amplifier and transmission part which can be adjusted in terms of its transmission properties between the microphone and the listener to different transmission characteristics.
  • a programmable hearing aid in a multi-channel design in which an arrangement of several signal branches is arranged behind the microphone receiving the input sound signals, each of which consists of a frequency-selective filter, a level-dependent gain control and an arrangement for non-linear signal deformation, followed by a summing amplifier that combines the partial signals, which is connected to an output signal converter (receiver) via an output amplifier is known from EP-B-0 071 845.
  • AGC automatic gain control
  • a hearing aid is also known in which the analog sound signal coming from the microphone, after passing through a low-pass filter in an A / D converter, is converted into a digital signal and fed to a discrete signal processing circuit, the transfer function of which n -th order from parameters stored in an electrically programmable read-only memory (EPROM) by means of a Microprocessor with arithmetic unit to adapt to hearing damage is controllable.
  • EPROM electrically programmable read-only memory
  • the programming can be changed by deleting the read-only memory and re-programming.
  • the digital signal modified in this way is then converted into a corresponding analog signal in a D / A converter, amplified and fed to the listener.
  • EP-B-0 064 042 discloses a circuit arrangement for a hearing aid in which, for example, the parameters of a number of different environmental situations are stored in a memory in the hearing aid itself.
  • a switch By actuating a switch, a first group of parameters is called up and controls, via a control unit, a signal processor connected between the microphone and the handset, which then sets a first transmission function intended for an intended environmental situation.
  • the transmission functions of several stored signal transmission programs can be called up in succession via a switch until the transmission function that is just right for the given environmental situation is found.
  • the object of the invention is to provide a hearing device which is characterized by a simplified, optimized control system.
  • this object is achieved in a hearing device of the type mentioned at the outset in that control functions are provided in the amplifier and transmission part, which are implemented in whole or in part according to the principle of neural structures.
  • control functions are provided in the amplifier and transmission part, which are implemented in whole or in part according to the principle of neural structures.
  • modern hearing aids also adapt the dynamic range of the input signal to the generally restricted dynamic range of the hearing impaired.
  • This requires specific control functions. These can be implemented using components that work according to the principle of neural structures and allow simple adjustment of the necessary controller characteristics. Among other things, this also enables the targeted introduction of non-linear components into the controller characteristics, as well as, under certain circumstances, the continuous optimization of the control behavior during operation.
  • the hearing aid 1 shown schematically in FIG. 1, records 2 sound signals x via a microphone. This acoustic information (input signal) is converted into electrical signals in the microphone. After signal processing in an amplification and transmission part 4, the electrical signal y is fed to a receiver 3 as an output converter.
  • Figure 1 shows the basic structure of an AGC control loop.
  • the output variable y to be controlled is tapped and processed (e.g. by rectification and formation of a suitable time average) and fed to a controller 5 '.
  • This controller 5 ' controls the amplification of the useful signal by means of an amplifier with variable amplification and determines the behavior of the AGC 31 through its control characteristics (including the so-called settling and decay time).
  • Signals 8 are tapped from the signal path of the hearing aid 1 between its microphone 2 and its receiver 3 at certain desired tapping points 7. These signals 8 are fed to a controller 5 ′ provided in the hearing aid, which is designed according to the principle of the neural structures 5.
  • the signals 8 first arrive at a module 9 for signal processing and from its outputs conditioned signals 10, 10 ', 10''are fed to the neural structure 5.
  • a data carrier 6 is assigned to the neural structure 5, in which configuration information of the neural structure is stored. Taking into account the configuration information of the data carrier 6, the neural structure 5 generates control signals 11 from the processed signals 10, 10 ′, 10 ′′, which are sent to the amplifier and transmission part 4 can be fed to adapt its transmission characteristics. According to the exemplary embodiment, these generated control signals 11 influence the processing of the variable to be controlled (useful signals).
  • signals are tapped from the signal path of the hearing aid at all relevant points or tapping points and processed in a suitable manner.
  • These processed signals and any other system information, e.g. whether the microphone or telephone operation is desired is fed into the neural structure.
  • the generated signals 11 thus influence the signal processing of the input signal x to the output signal y.
  • the behavior of the neural structures does not necessarily have to be unchangeable (i.e. completely described by the hardware structure), but can be configurable (e.g. by programming).
  • configuration information can be stored in the memory 6 in the hearing device 1.
  • means 13 are provided for detecting system states of the hearing device 1, the output signals 14 of which can be fed to the module 9 for signal processing and / or the neural structure 5, these output signals being able to be taken into account when generating the control signals 11.
  • Control elements that can be actuated by the hearing aid wearer can be provided as means 13 for detecting system states of the hearing aid 1.
  • the hearing aid can then be equipped, for example, with a situation switch, which enables the hearing aid wearer - as described at the beginning of EP-B-0 064 042 - to select a stored transmission function which he thinks is suitable for the given environmental situation.
  • the hearing aid can have, for example, a switch for switching from microphone operation to telephone coil operation.
  • the hearing aid also has a volume control with which the hearing impaired person influences the volume.
  • the hearing device can also be characterized in that a device 15 which monitors the state of charge of the hearing device battery, not shown, is additionally provided as a means for detecting system states of the hearing device 1. Thereafter, it is possible that the respective state of charge of the hearing aid battery is also taken into account when generating the output signals 11 of the neural structure 5.
  • the signals 8 are tapped from the signal paths formed by the individual channels.
  • the signals 8 from the signal paths and the output signals 14 of the means 13, 13 'for detecting system states of the hearing aid e.g. prepared by rectification, formation of suitable averages over time and possibly from their derivations.
  • signal processing - module 9 at least one input variable, e.g.
  • the signals 14 can also be fed directly to the neural structure (FIG. 2).
  • Neural structures consist of many similar elements or neurons 19. The function of the neural structure as a whole essentially depends on the way in which these neurons are interconnected.
  • the course of the output function W represents a step function at the threshold value s.
  • the output function W has a continuous course around the threshold value s.
  • FIG. 5b shows a continuous, so-called sigmoid curve of the output variable with limitation to a maximum and a minimum output value.
  • FIG. 5c shows a linear course in the transition area.
  • the signals which are processed by the neural structure can be designed as voltage signals, current signals or as frequency-variable pulse signals. In the latter In this case, the signal may have to be converted into a continuous current or voltage signal and back again at some points in the neural structure with the aid of suitable circuits.
  • FIG. 6 shows the exemplary connection of three neurons 19 to the typical structure of a single-layer feedback network with the inputs e i (t) and the outputs a j (t + ⁇ T).
  • FIG. 7 shows an example of the structure of a multilayer feedback-free network. Depending on the function of the neural structure to be implemented, one or the other network structure must be used. Mixed forms of both structures are also possible.
  • the function of a neural structure as a whole is essentially determined by the network structure and by the weighting functions of the input signals on each neuron 19. These parameters can be permanently set through the implementation in terms of circuitry if constant behavior is desired. If, on the other hand, a change in behavior should be possible, some or all of these parameters must be programmable. Their respective values must then be stored in a configuration memory or data carrier 6. The individual memory elements can be arranged in a concentrated form or locally assigned to the respective neuron.
  • the stored parameters can be modified either by external programming of the memory elements and / or by an algorithm implemented in the circuit. Modification is also possible during the ongoing operation of the neural structure.
  • Figure 8 shows an example of the circuitry implementation of a single-layer feedback network.
  • Amplifiers 24 with complementary outputs act as threshold elements.
  • the connections (synapses) between the outputs and inputs of the neurons are weighted using the guide values R ij .
  • the addition of the input signals for each neuron happens in the circuit nodes at the input of each amplifier.
  • the output signals of the amplifiers and thus the neural structure are the voltage signals U i .
  • E1 to e4 denote the inputs of the circuit and a1 to a4 denote inverting and non-inverting outputs of the circuit.
  • FIG. 9 shows a possible circuit implementation of a synapse (weighted input of a neuron) with programmable connection strength. Only the connection strengths +1, -1 and 0 are possible and the signals to be transmitted from this synapse can only assume the logical values 0 and 1. If both memory cells 25, 26 are programmed so that they block the respective associated switching transistor 27 or 28, output a is independent of input e; the synapse therefore represents an interruption (connection strength 0). If, on the other hand, the memory cell 25 is programmed to close the switch and the memory cell 26 to open the associated switch, then a current (logic 1) flows from the output a when the input is logic 1, and no current (logic 0) if the input is logic 0.
  • the synapse acts as a connection of strength +1. If both memory cells 25, 26 are programmed inversely for this purpose, the inverse logic behavior results. The synapse then acts as a connection of strength -1. V dd indicates the circuit connection to the supply voltage in the drawing.
  • FIG. 10 shows a possible implementation of a programmable synapse with variable connection strength. She works on the principle of the multiplier.
  • the strength of the synaptic connection is stored as the difference between two analog voltage values on two capacitors 29, 30.
  • fuzzy logic can be used to prepare the input variables of the neural structure according to certain predefinable rules.
  • Parts of the control behavior that can be specified explicitly can also be implemented as fuzzy logic, while additional parts that cannot be formulated explicitly, e.g. Parts of the control behavior learned during operation can be realized by the neural structure.
  • these two components of the controller would then preferably be connected in parallel.
  • a memory with configuration information, which determines the behavior of the respective component, can in turn be assigned to both components.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Automation & Control Theory (AREA)
  • Evolutionary Computation (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Neurosurgery (AREA)
  • Otolaryngology (AREA)
  • Acoustics & Sound (AREA)
  • Signal Processing (AREA)
  • Feedback Control In General (AREA)
EP94117796A 1994-11-10 1994-11-10 Prothèse auditive Withdrawn EP0712262A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP94117796A EP0712262A1 (fr) 1994-11-10 1994-11-10 Prothèse auditive

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP94117796A EP0712262A1 (fr) 1994-11-10 1994-11-10 Prothèse auditive

Publications (1)

Publication Number Publication Date
EP0712262A1 true EP0712262A1 (fr) 1996-05-15

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EP94117796A Withdrawn EP0712262A1 (fr) 1994-11-10 1994-11-10 Prothèse auditive

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6044163A (en) * 1996-06-21 2000-03-28 Siemens Audiologische Technik Gmbh Hearing aid having a digitally constructed calculating unit employing a neural structure
EP1331835A2 (fr) * 2002-01-08 2003-07-30 Knowles Electronics, Inc. Amplificateur à gain numérique programmable

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1989008353A1 (fr) * 1988-02-23 1989-09-08 Resound Corporation Systeme ameliore de compression programmable a bandes multiples
US5101361A (en) * 1989-09-29 1992-03-31 The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration Analog hardware for delta-backpropagation neural networks
EP0540168A2 (fr) * 1991-10-31 1993-05-05 Kabushiki Kaisha Toshiba Simulateur utilisant un réseau neuronal
US5253300A (en) * 1991-03-22 1993-10-12 H. C. Knapp Sound Technology Inc. Solar powered hearing aid

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1989008353A1 (fr) * 1988-02-23 1989-09-08 Resound Corporation Systeme ameliore de compression programmable a bandes multiples
US5101361A (en) * 1989-09-29 1992-03-31 The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration Analog hardware for delta-backpropagation neural networks
US5253300A (en) * 1991-03-22 1993-10-12 H. C. Knapp Sound Technology Inc. Solar powered hearing aid
EP0540168A2 (fr) * 1991-10-31 1993-05-05 Kabushiki Kaisha Toshiba Simulateur utilisant un réseau neuronal

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
G.TRAUTZL: "NEURONALE NETZE UNTERSTÜTZEN FUZZY LOGIK TOOL", ELEKTRONIK, vol. 41, no. 2, 21 January 1992 (1992-01-21), GERMANY, pages 100 - 101, XP000381757 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6044163A (en) * 1996-06-21 2000-03-28 Siemens Audiologische Technik Gmbh Hearing aid having a digitally constructed calculating unit employing a neural structure
EP1331835A2 (fr) * 2002-01-08 2003-07-30 Knowles Electronics, Inc. Amplificateur à gain numérique programmable
EP1331835A3 (fr) * 2002-01-08 2004-10-20 Knowles Electronics, LLC Amplificateur à gain numérique programmable

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