Method for operating a hearing device, and hearing device

Abstract

A hearing device has a signal processor which has an adjustable parameter that has a given setting at a given time. The parameter is set depending on the situation by selecting a setting, depending on an environmental situation and by a learning machine. A current setting of the parameter can be rated by feedback from a user. In a first training procedure the learning machine is passively trained by negative feedback signals, by rating feedback from the user as dissatisfaction with the current setting and by assuming the user's satisfaction with the current setting as long as no feedback is given. In a second training procedure the learning machine is trained by changing the current setting independently of the feedback from the user and in spite of an assumed satisfaction with the current setting, so that the user is offered a different setting which can then be rated by feedback.

Claims

1. A method for operating a hearing device having a signal processor with at least one adjustable parameter having a given setting at a given time, which comprises the steps of: generating the adjustable parameter according to a situation by selecting a setting for the adjustable parameter depending on a current environment situation, by using a learning machine; rating a current setting of the adjustable parameter rated by feedback of a user of the hearing device; passively training the learning machine in a first training procedure by use of negative feedback, being the feedback from the user being rated as dissatisfaction with the current setting and by assuming the user to be satisfied with the current setting as long as no said feedback is given; and additionally training the learning machine in a second training procedure by changing the current setting independently of the feedback from the user and in spite of an assumed satisfaction with the current setting, so that the user is offered a different setting which can then be rated by the feedback, wherein the different setting is selected depending on a previous rating of the setting compared to other settings.

2. The method according to claim 1, wherein in an event of user satisfaction with the setting the learning machine increases a rating of the setting, and decreases the rating in an event of user dissatisfaction.

3. The method according to claim 1, wherein the learning machine automatically assumes user satisfaction with the current setting if no said feedback has been issued over a certain period of time.

4. The method according to claim 1, wherein the second training procedure of the learning machine is passive, by not actively eliciting the feedback from the user.

5. The method according to claim 1, wherein in the second training procedure, the different setting differs from the current setting by no more than 10% higher or 10% lower.

6. The method according to claim 1, wherein in the second training procedure, the different setting is selected depending on the previous rating of the setting by other users.

7. The method according to claim 1, which further comprises carrying out the first and the second training procedure during normal operation of the hearing device.

8. The method according to claim 1, which further comprises actively training the learning machine in a third training procedure by eliciting the feedback from the user to rate the current setting.

9. The method according to claim 1, wherein the feedback consists of the user changing the adjustable parameter.

10. The method according to claim 1, wherein the feedback includes one of the following actions by the user: changing a volume of the hearing device, changing a program of the hearing device, and changing a focus of the hearing device.

11. The method according to claim 1, wherein the learning machine is integrated into the hearing device.

12. A hearing device, comprising: a signal processing unit configured to carry out a method according to claim 1.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

(1) FIG. 1 is a block diagram showing a hearing device according to the invention;

(2) FIG. 2 is a block diagram shows a method for operating the hearing device; and

(3) FIG. 3 is a block diagram showing a training procedure of a training machine.

DETAILED DESCRIPTION OF THE INVENTION

(4) Referring now to the figures of the drawings in detail and first, particularly to FIG. 1 thereof, there is shown a hearing device 2, which has a signal processor 4 having at least one adjustable parameter P, which has a given setting E at a given time, i.e. a specific value for the parameter P, e.g. a specific gain or volume. A user, not shown in detail, of the hearing device 2 wears it in or on the ear when the device is used as intended. The hearing 2 has at least one microphone 6 for receiving ambient sounds, and a receiver 8 for outputting sounds to the user. The microphone 6 generates an electrical input signal from the ambient noise, which is passed on to the signal processor 4 and then modified, e.g. amplified, by the latter depending on the parameter P. This generates a modified input signal which is then an electrical output signal and which is forwarded to the receiver 8 for output. In the present case, the signal processor 4 has a modification unit 9 which modifies the input signal as a function of the parameter P.

(5) In the method for operating the hearing device 2 a parameter P is set according to the situation by selecting the most suitable possible setting E for the parameter P as a function of the current environment situation and using a learning machine 10. This is carried out repeatedly and automatically by the signal processor 4 and as part of the operation of the hearing device 2. In addition, the parameter P in this case is also manually adjustable by the user via an input element 12. FIG. 2 shows an exemplary embodiment of the method. For the situation-dependent setting, in a first step S1 the current environment situation is first detected. The environment situation is assigned a specific setting E according to an assignment rule, which is then selected in a second step S2 so that the parameter P is set accordingly.

(6) In step S1, the environment situation is detected by means of a classifier 14 of the learning machine 10. The classifier 14 analyzes the input signal generated by the microphone and assigns a class to the current environment situation. Depending on the class, the parameter P is then set in the second step S2. By means of the learning machine 10, the hearing device 2 learns over time which setting E is most suitable in which environment situation and then selects that setting. The learning takes place in a third step S3 in parallel with the two steps S1 and S2 and influences the selection of the setting E for the parameter P in the second step S2, as shown in FIG. 2. The particular setting E is therefore not assigned statically to a particular environment situation in the present case, but is adjusted dynamically by the learning machine 10.

(7) A current setting E of the parameter P can be rated by feedback R of a user of the hearing device 2. The current setting E is the setting E which is set at the current time. In a fourth step S4, the user can rate this setting E by means of feedback R. The feedback R generally comprises an instruction or request from the user to the hearing device 2 to change the current setting E. The feedback R is provided in the present case via the input element 12 of the hearing device 2, e.g. a button for manual input or a microphone, e.g. the microphone 6, for speech input, or another type of sensor for acquiring a user input. The user expresses his/her satisfaction with the current setting E via the feedback R. A rating is then assigned to a particular setting E of the parameter P, e.g. in the form of a counter. The rating is then changed depending on the feedback R and indicates the satisfaction of the user with a respective setting E for the assigned environment situation.

(8) The method contains a learning procedure for the learning machine 10. An exemplary embodiment of this method is explained below with reference to FIG. 3. In a first training procedure the learning machine 10 is passively trained using negative feedback R, by feedback R from the user being rated in step B− as dissatisfaction with the current setting E, and by assuming in step B+ the user to be satisfied with the current setting E as long as no feedback R is given. No feedback R from the user is explicitly elicited or requested, but voluntarily provided feedback R from the user is used instead.

(9) In addition, in the exemplary embodiment shown, in a second training procedure the learning machine 10 is additionally trained by changing, in a fifth step S5, the current setting E independently of feedback R from the user and in spite of an assumed satisfaction with the current setting E, so that the user is offered a different setting E which can then be appropriately rated by feedback R. Based on the first, passive training procedure, the user is thus offered different settings E unprompted, in order to obtain additional ratings for these settings E in steps B−, B+, although the current setting E itself is assumed to be satisfactory. The current setting E of the parameter P is therefore changed under a constant environment situation to test different settings E for the same environment situation, i.e. the learning machine 10 experiments with different settings E, so that the second training is also called experimentation-based training. The experimentation-based training using the fifth step S5 will potentially provoke additional feedback responses R and thus then potentially generate additional ratings in steps B−, B+, but in doing so the advantage of a passive training is retained, namely the reduced user interaction compared to active training.

(10) In the present case the second training procedure of the learning machine 10 is also passive, by virtue of no feedback R being actively elicited from the user. Therefore, even during the second training procedure, feedback R is not actively requested from the user, rather it is sufficient that the other setting E can be rated. The user can rate this other setting E, but does not have to do so. In this case, even the same mechanism is used for the rating as for the first training. In any case, the learning machine 10 therefore rates a feedback R as dissatisfaction with the setting immediately before the feedback R or at the time of the feedback R, and not as satisfaction with the setting immediately after the feedback R, if the user has changed the setting E as part of the feedback R.

(11) Overall, if the user is satisfied with a setting E the learning machine 10 increases a rating of this setting E, and decreases it if the user is dissatisfied. As a result the fitness for purpose of the individual settings E is stored in the form of a set of ratings, in order then to select the optimal setting E in the situation-dependent setting of the parameter P in the second step S2. If the environment situation changes, the new environment situation is detected and the setting E which has the highest rating for this environment situation is then selected. If the environment situation remains the same, other settings E, which in principle are rated as worse, are set and therefore tested.

(12) In the present case the learning machine 10 automatically assumes the user's satisfaction with the current setting E if no feedback R has been provided over a certain period of time t. This is also the case in the exemplary embodiment of FIG. 3. The automatic assumption of the user's satisfaction after a certain period of time t without the user changing the setting E allows a positive rating without active user interaction. For example, the time period t that is allowed to elapse is between 5 minutes and 15 minutes.

(13) The other setting E, which is presented to the user unprompted within the experimentation-based training, can essentially be selected as desired or at random, but a specific selection is made in this case. The other setting E in this case is selected depending on a previous rating of this setting E compared to other settings E. For example, a setting E is selected that has a lower number of ratings at least for the current environment situation than the current setting E, in order then to potentially obtain further ratings.

(14) Alternatively or in addition, the other setting E is selected as a function of its similarity to the current setting E and differs from the current setting E by no more than 10%, for example, hence is similar thereto. For example, the parameter P is a sound volume and the setting E a value for this volume, which is then varied within a range of +/−10% by the experimentation-based training.

(15) Alternatively or in addition, the other setting E is selected as a function of its rating by other users. In an exemplary extension, the selection is further constrained by only taking into account the evaluations of such other users who are similar to the user, e.g. who have a similar audiogram or belong to a similar population group or are of similar age.

(16) Apart from the exemplary embodiment shown with purely modified passive training, in one variant this is combined with an active training procedure. In a third training procedure the learning machine 10 is then additionally actively trained by eliciting feedback R from the user to rate the current setting E. The active training procedure is performed in a time-dependent or situation-dependent manner, or initiated by the user him/herself. For example, the active training is performed at certain times or after a certain time interval has elapsed, or when the environment situation has changed.

(17) The feedback E of the user in this case consists of the user manually changing the parameter P using the input element 12. In a variant not shown, the input element 12 is not a part of the hearing device 2 as shown in FIG. 1, but a part of an auxiliary device connected to the hearing device 2 for data transfer. The auxiliary device is e.g. a remote control for the hearing device 2, or a smartphone or similar. The manual setting E of the parameter P by means of the input element 12 is also shown in FIG. 3. In case of dissatisfaction with the setting E, the user can thereby change the parameter P. This is then treated by the learning machine 10 as dissatisfaction with the setting E that was applied immediately before the feedback R, and its rating is reduced accordingly in step B−. The feedback signal R is then used to set a new setting E. In an extension, it is additionally assumed that this new setting E is satisfactory to the user, since the user has clearly specifically chosen this setting E, i.e. a satisfaction with the new setting E is assumed and its rating is increased accordingly in a step B+. This variant is not explicitly shown in FIG. 3.

(18) The feedback R comprises, for example, one of the following actions on the part of the user: changing the volume of the hearing device 2, changing a program of the hearing device 2, changing the focus of the hearing device 2. In addition, further actions are also conceivable and appropriate.

(19) For example, the learning machine 10 is a neural network, a support vector machine, or similar. The learning machine 10 in this case is designed as an integrated circuit, e.g. based on software engineering as a microcontroller, or in electrical circuit technology as an ASIC. In this case, the learning machine 10 is integrated into the hearing device 2, in the exemplary embodiment shown even as part of the signal processor 4. Alternatively, in another suitable arrangement, not shown, the learning machine 10 is relocated to an auxiliary device, e.g. as described above, which is connected to the hearing device 2, e.g. wirelessly.

(20) The various aspects described above and shown in FIGS. 1-3 can in principle also be implemented independently of each other and in principle can also be combined as desired, thus also resulting in further exemplary embodiments.

(21) The following is a summary list of reference numerals and the corresponding structure used in the above description of the invention: 2 hearing device 4 signal processor 6 microphone 8 receiver 9 modification unit 10 learning machine 12 input element 14 classifier B−, B+ step (for rating) E setting P parameter R feedback S1 first step S2 second step S3 third step S4 fourth step S5 fifth step (change of the current setting for second training procedure) t time interval