Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS
  • H04R25/00—Electric hearing aids
  • H04R25/70—Adaptation of deaf aid to hearing loss, e.g. initial electronic fitting
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; 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; ELECTRIC HEARING AIDS; 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; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS
  • H04R25/00—Electric hearing aids
  • H04R25/50—Customised settings for obtaining desired overall acoustical characteristics
  • H04R25/505—Customised settings for obtaining desired overall acoustical characteristics using digital signal processing
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS
  • H04R25/00—Electric hearing aids
  • 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

Definitions

• the present disclosure relates to hearing devices and related tools, methods, and systems in particular for one or more of determining, tuning, fitting and optimizing hearing device parameters and models therefore.
• a fitting agent for a hearing device system comprising a hearing device is provided.
• Fitting and tuning of hearing devices or hearing aids has always been considered a tedious task of healthcare professionals (HCPs).
• Traditional approaches for fitting hearing device parameters rely on compensation of a user's hearing loss, based on audiograms, by applying rules such as NAL-NL1 or NAL-NL2. These rules, however, do not take into account specific user preferences.
• Recent approaches involve preference learning for hearing devices.
• the present disclosure takes into account any differences in user preference in different environments and provides effective and memory-efficient user preference model update.
• the need for storing historical and specific user feedback may be eliminated in turn reducing the memory requirements.
• environment-dependent user preference functions may provide simpler user preference functions in turn allowing a simple and effective way to configure one or more hearing device parameters of a hearing device.
• the present disclosure provides an improved listening experience to the user by improving the modelling of the users preferred hearing parameter settings in different environments in turn resulting in optimized settings being applied in the hearing device, which in turn allows for an improved user experience.
• the present disclosure provides an efficient automated search for optimal hearing device parameters by incorporating a user feedback into the learning cycle.
• a fitting agent is provided, that allows to learn user preferences for hearing device parameters in an efficient and minimally obtrusive way by empowering the user to take direct decisions and have direct impact on the fitting and/or tuning process.
• hearing device parameters can be configured, such as fitted and/or tuned, during a normal operating situation and/or with a small number of user inputs/interactions.
• hearing device parameters can be configured, such as fitted and/or tuned, during a normal operating situation and/or with a small number of user inputs/interactions.
• the hearing device users receive fully personalized settings for their hearing device, based on preferences in the user's own personal customized environments.
• the user's preferences are used to define and adjust the user's environments and thus also the corresponding hearing device settings or operational modes, which in turn increases user satisfaction and leads to more regular use of the hearing device.
• the present disclosure provides for environment personalization/customization and leads to improved system performance. This is achieved by introducing a two-way dependency between environment domain and (user) preference domains providing a significant performance improvement with respect to the baseline model, i.e. a fitting agent without feedback from the user into the environment model.
• a fitting agent is disclosed.
• the fitting agent or at least a first part thereof may be implemented, e.g. as an application, in an accessory device, such as an electronic device.
• the accessory device comprises an interface, a processor, and a memory.
• the accessory device may for example be or comprise a mobile phone, such as a smartphone, a smartwatch, a special purpose device, a computer, such as a laptop computer or PC, or a tablet computer.
• the fitting agent or at least a second part thereof may be implemented in a server device.
• the fitting agent or at least a third part thereof may be implemented in a hearing device.
• the fitting agent may be implemented in one or more electronic devices, such as a hearing device and/or accessory device(s).
• the hearing device system may comprise a server device and/or a fitting device.
• the fitting device is controlled by a dispenser and is configured to determine configuration data, such as fitting parameters.
• the server device may be controlled by the hearing device manufacturer.
• the fitting agent may be a fitting agent for a hearing device system comprising a hearing device worn by a hearing device user.
• the fitting agent may also be denoted a hearing device fitting agent.
• the fitting agent comprises one or more processors.
• the one or more processors are configured to initialize a user model and an environment model.
• the environment model is also denoted environmental model.
• the user model and/or the environment model may be retrieved from a memory of the fitting agent.
• the user model comprises a plurality of user preference functions and associated user response distribution.
• the user response distribution is optionally state-dependent and can be formulated as a single distribution, where depending on the state the distribution has a respective state-dependent form.
• the environment model defines or comprises, at least after one step of user feedback, a number of active (observed) environment classes or clusters and/or non-active environment classes or clusters.
• Each active environment class may correspond to a (single) user preference function.
• the non-active environment classes are optionally infinite and may be bundled in one single non-active class or cluster. Thereby, spin-out of a new cluster as an active cluster from the non-active cluster is always possible.
• the environment model may be said to comprise an infinite number of classes or clusters.
• class and cluster may be used interchangeably herein.
• the user model optionally represents one or a plurality of probabilistic descriptions of user responses, when comparing two sets of hearing device parameter settings. Integral parts of the user model include one or more, such as a plurality of user preference functions and one or more, such as a plurality of distributions of the user responses, also denoted user response distributions, to the presented choices of parameters.
• the user model comprises one or more distributions of parameters of the respective user preference functions. In other words, each user preference function has an associated distribution of hearing device parameters.
• the user model may include a first user preference function and associated first user response distribution, wherein the first user preference function is optionally associated with a first environment class or cluster of the environment model.
• a vector of hearing device parameters is optionally defined on a D-dimensional continuous compact surface.
• hearing device parameters x are optionally defined on a D-dimensional hyper-cube, i.e., x e [0,1] D .
• the hearing device parameters may be normalized by their physical range.
• the fitting agent is configured to find optimized/improved values of hearing device parameters, also denoted ⁇ for a particular user.
• the number D of hearing device parameters may be 1 and/or less than 100, such as in the range from 10 to 50.
• the number D of hearing device parameters may be larger than 20, such as in the range from 25 to 75.
• the user preference functions or at least one or more of the user preference functions of the K user preference functions may be unimodal.
• 0,1 d x is the cumulative density function of the standard normal distribution, and ⁇ is a sample from the normal distribution with mean vector ⁇ k and covariance matrix ⁇ k . Values of the mean and covariances may be learned from the user responses.
• the real-valued exponent v may be in the range from 0.01 to 0.99.
• the real-valued exponent v may be less than 0.5 such as in the range from 0.01 to 0.45.
• the real-valued exponent v may be larger than 0.5 such as in the range from 0.55 to 0.99.
• the real-valued exponent v may be in the range from 0.25 to 0.75, such as 0.5.
• ⁇ k , ⁇ k are also denoted user preference function parameters, where ⁇ k is an optimum of the user preference function that corresponds to the optimal user-specific hearing device parameters and ⁇ k ⁇ is a diagonal matrix indicative of a user sensitivity, i.e. ability to distinguish change in the parameter. It is noted that other unimodal user preference functions are also possible.
• the fitting agent is configured to obtain a primary test setting also denoted x_ref, x ref , or ⁇ t for the hearing device.
• the primary test setting x_ref is a vector comprising D hearing device parameters for the hearing device.
• the hearing device parameters may comprise one or more of filter coefficients, compressor settings, gains, or other parameters relevant for the operation of or signal processing in the hearing device.
• the primary test setting may be based on and/or dependent on the present environment as indicated by the environment state determined via the environment model. In other words, the primary test setting may be based on and/or dependent on the environment state also denoted c(t), t indicating time step.
• To present the primary test setting and the secondary test setting to a user optionally comprises to output a secondary test signal also denoted y' t according to the secondary test setting.
• the secondary test signal may be an audio signal.
• the secondary test signal may be output via loudspeaker or receiver of a hearing device.
• To present the primary test setting and the secondary test setting to a user optionally comprises to generate the secondary test signal according to the secondary test setting in accessory device and to stream the secondary test signal from accessory device to hearing device.
• To present the primary test setting and the secondary test setting to a user optionally comprises to transmit a control signal indicative of secondary test signal/secondary test setting from accessory device to hearing device.
• the control signal may include secondary test setting.
• the fitting agent is configured to determine and update hearing device parameters of the hearing device based on the updated user model and optionally the updated environment model.
• a fitting agent for a hearing device system comprising a hearing device worn by a hearing device user
• the fitting agent comprises one or more processors configured to initialize a user model comprising a plurality of user preference functions and associated user response distribution; initialize an environment model; obtain environment data indicative of a present environment; determine, using the environment model, an environment state based on the environment data; obtain a test setting comprising a primary test setting and a secondary test setting for the hearing device based on the environment state; present the test setting to the hearing device user; obtain a user input of a preferred test setting indicative of a preference for either the primary test setting or the secondary test setting; update the user model for provision of an updated user model based on the preferred test setting and the environment state; and update the environment model for provision of an updated environment model based on the preferred test setting and one of the user model and the updated user model.
• to update the environment model comprises to determine an updated environment state based on one or more of the preferred test setting, the user model, and the updated user model, and to update the environment model based on the updated environment state.
• the updated environment state is based on the preferred test setting and the updated user model.
• to update the environment model is optionally based on an updated environment state.
• to update the environment model comprises to reduce the number of environment classes of the environment model. Reducing the number of environment classes or clusters of the environment model may be done by merging at least two classes of the environment model. For example, two active clusters may be merged to form one active cluster. For example, an active cluster may be discarded by merging the active cluster with the non-active cluster.
• the environment model is a hierarchical Dirichlet process Hidden Markov Model (HDP-HMM).
• HDP-HMM Hierarchical Dirichlet process Hidden Markov Model
• the environment classes are modelled as latent random variables, e.g. from a countably infinite class space.
• to update the environment model comprises to apply a Hidden Markov Model, such as a sticky hierarchical Dirichlet process Hidden Markov Model.
• a Hidden Markov Model such as a sticky hierarchical Dirichlet process Hidden Markov Model.
• the fitting agent/one or more processors of the fitting agent is/are configured to obtain environment data indicative of a present environment and determine, e.g. using the environment model, an environment state based on the environment data.
• to obtain environment data comprises to obtain audio data and optionally determining the environment data based on the audio data and/or including the audio data in the environment data.
• one or more processors of the fitting agent may be configured to obtain audio data and determining the environment data or at least one or more environment parameters based on the audio data.
• Audio data may comprise first audio data representing or being indicative of audio recorded by one or more microphones of a hearing device of the user.
• Audio data may comprise second audio data representing or being indicative of audio recorded by one or more microphones of an accessory device or accessory devices of the user.
• Audio data may comprise third audio data representing or being indicative of audio wirelessly transmitted to a hearing device of the user.
• the fitting agent may be configured to classify the environment based on hearing device audio and set one or more environment identifiers and/or environment probabilities of the environment data accordingly.
• to obtain environment data comprises to obtain context data and optionally determining the environment data based on the context data and/or including the context data in the environment data.
• one or more processors of the fitting agent may be configured to obtain context data and optionally determining the environment data based on the context data.
• Context data may be indicative of the context in which the user is in, such as indicative of a user's location, position, movement, temperature, pulse, or other data relevant for the environment.
• the context data may comprise location data, e.g. GPS coordinates, and/or movement data, such as accelerometer data.
• the context data may comprise calendar data, and the environment data may be based on the calendar data.
• the context data may comprise sensor data, e.g.
• the context data may comprise hearing device data transmitted from the hearing device, such as one or more program identifiers, one or more operating parameters, and/or one or operating mode identifiers of the hearing device.
• to obtain environment data comprises to receive user input indicative of the environment, e.g. via a user interface of an accessory device.
• the user may select and indicate the present environment via accessory device, e.g. from a list of environments presented on a touch-display of the accessory device.
• the fitting agent/one or more processors of the fitting agent is/are configured to update the user model, such as one or more user preference functions f k ( x ; ⁇ k , ⁇ k ) and/or one or more user response distributions P u , , based on hearing device parameters of the preferred test setting and the environment state.
• a user response distribution P u may model a user response in one or more environments.
• a user response distribution may be a weighted user response distribution for all or a subset of environments, e.g.
• the fitting agent is configured to update the user model based on hearing device parameters of the preferred test setting, a non-preferred test setting and optionally the environment state c(t).
• the environment state c(t) is indicative of a hard decision or a soft decision.
• the environment state may be an identifier of a state (hard decision) or be indicative of an environment state distribution (soft decision).
• the fitting agent may be configured to update the user model based on (r, x_ref, x_alt, c(t)).
• To update the user model may comprise to update the user preference function, or at least parameters thereof and/or to update parameter distributions associated with user preference functions, based on environment state and/or its reliability (soft) and one or more of the primary test setting, the secondary test setting and the user input of a preferred test setting.
• the parameters of the user preference function and/or associated parameter distributions may be updated based on the result of the trial including the primary test setting, the secondary test setting, the preferred test setting of the primary test setting and the secondary test setting, and environment state.
• To update the user model may comprise to update the user response distribution(s), or at least parameters thereof, based on environment state and one or more of the primary test setting, the secondary test setting and the user input of a preferred test setting.

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• Health & Medical Sciences (AREA)
• General Health & Medical Sciences (AREA)
• Neurosurgery (AREA)
• Otolaryngology (AREA)
• Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• Acoustics & Sound (AREA)
• Signal Processing (AREA)
• User Interface Of Digital Computer (AREA)

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Citations (6)

• 2024
  • 2024-01-15 EP EP24151985.9A patent/EP4586647A1/de active Pending

Patent Citations (6)

Non-Patent Citations (6)

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