Method for operating a hearing system, hearing system and hearing device

Abstract

A method operates a hearing system having a hearing device and modifies an input signal for the purpose of sound output to a user and, applies multiple algorithms with a respective potency, as a result of which a respective algorithm is applied with a present potency in a present situation. The hearing system recurrently receives a report from the user indicating that the user is dissatisfied with the sound output in the present situation. The hearing system has a database, containing multiple weights for each algorithm, to rate a change of the potency. If a report is received, each of the algorithms is rated using the weights to ascertain an individual-case relevance for each of the algorithms, to assess the effect of a change of the potency. Multiple individual-case relevances are combined to form a relevance value for each algorithm, the relevance values are compared with one another.

Claims

1. A method for operating a hearing system having a hearing device and a database, which comprises the steps of: configuring the hearing device to modify an input signal for a purpose of sound output to a user and, to that end, to apply multiple algorithms with a respective potency, as a result of which a respective algorithm of the algorithms is applied with a present potency in a present situation; configuring the hearing system to recurrently receive a report from the user indicating that the user is dissatisfied with the sound output in the present situation; configuring the database to contain a plurality of weights for each of the algorithms, in order to affect a rate of change of the respective potency, wherein, if the report is received, each of the algorithms is rated by using the weights to ascertain an individual-case relevance for each of the algorithms, in order to assess an effect of a change of the respective potency in the present situation; and combining multiple individual-case relevances to form a relevance value for each of the algorithms, relevance values are compared with one another, this is taken as a basis for selecting a most relevant algorithm, and then an adapted potency is used for the most relevant algorithm by adapting the present potency of the algorithm for a recommended potency determined on a basis of the weights.

2. The method according to claim 1, wherein each of the algorithms has at least one assigned signal feature and the present potency of the respective algorithm is set depending on a situation by setting the present potency on a basis of a strength of the at least one assigned signal feature in the input signal in the present situation.

3. The method according to claim 2, wherein the database is in a form such that the strength of the at least one assigned signal feature is taken into consideration for ascertaining the individual-case relevance and the recommended potency.

4. The method according to claim 1, wherein a respective weight of the weights indicates what proportion of users in a reference group prefers an associated change.

5. The method according to claim 4, wherein the reference group contains only the users who are similar to the user.

6. The method according to claim 1, wherein the recommended potency is calculated from the weights by means of a statistical evaluation.

7. The method according to claim 1, which further comprises calculating the individual-case relevance on a basis of a potency difference, which is a difference between the present potency and the recommended potency.

8. The method according to claim 1, which further comprises calculating the individual-case relevance on a basis of a change recommendation, which is a measure of a sum of the weights for changing to a different potency, on the one hand, compared with the weight for retaining the present potency, on the other.

9. The method according to claim 1, which further comprises calculating the individual-case relevance on a basis of a measure of scatter for the present potency.

10. The method according to claim 1, which further comprises calculating the relevance value of the respective algorithm from the individual-case relevances of the respective algorithm by means of a statistical evaluation.

11. The method according to claim 1, which further comprises adapting the present potency of the most relevant algorithm for the recommended potency only when the relevance value of the most relevant algorithm differs from the relevance values of other ones of the algorithms by at least a minimum value.

12. The method according to claim 1, which further comprises updating the weights in the database on a basis of the adapted potency and the adapted potency is therefore taken into consideration from then on for ascertaining the individual-case relevance and the recommended potency.

13. The method according to claim 1, wherein the adapted potency is proposed to the user in a test mode and is used as a new present potency only after a confirmation by the user.

14. The method according to claim 13, wherein a different, experimental potency is occasionally proposed in the test mode instead of the adapted potency.

15. The method according to claim 4, wherein the reference group contains only the users for whom a similar audiogram to that for the user was ascertained.

16. The method according to claim 1, wherein the recommended potency is calculated from the weights by means of a mean value formation or a median value formation.

17. The method according to claim 1, which further comprises calculating the relevance value of the respective algorithm from the individual-case relevances of the respective algorithm by means of a median value formation.

18. A hearing system or hearing device configured to carry out a method according to claim 1.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

(1) FIG. 1 is an illustration showing a hearing system according to the invention;

(2) FIG. 2 is a block diagram of a hearing device;

(3) FIG. 3 is a flow chart for explaining a method according to the invention;

(4) FIG. 4 is an illustration showing a three-dimensional weight matrix;

(5) FIG. 5 is an illustration showing an excerpt from the weight matrix from FIG. 4; and

(6) FIG. 6 is an illustration showing a further excerpt from the weight matrix from FIG. 4.

DETAILED DESCRIPTION OF THE INVENTION

(7) Referring now to the figures of the drawings in detail and first, particularly to FIG. 1 thereof, there is shown an exemplary embodiment of a hearing system 2 that has a hearing device 4, and also a supplementary device 6 and a server 8 with a database 10. The hearing device 4 is shown schematically in FIG. 2. The hearing device 2 is configured to modify an input signal 12 for the purpose of sound output to a user, who is not shown explicitly, and, to that end, to apply multiple algorithms 14 with a respective potency (effectiveness) W, as a result of which a respective algorithm 14 is applied with a present potency aW in a present situation. The hearing device 4 shown has at least one microphone 16 that picks up sound from the surroundings and generates the electrical input signal 12. The input signal is supplied to signal processing 18 of the hearing device 4, for processing, i.e. for modification. The signal processing 18 is a part of a control unit 20 of the hearing device 4. The hearing device 4 is used here to cater for a user with impaired hearing. To this end, the processing is affected on the basis of an audiogram for the user associated with the hearing device 4, as a result of which an individual hearing deficiency of the user is compensated for. The signal processing 18 outputs an electrical output signal 22 as result, the output signal then being converted back into sound via a receiver 24 of the hearing device 4 and output to the user, as a result of which a sound output is affected. The hearing device 4 shown in FIG. 1 is a binaural hearing device 4, having two individual devices that each have at least one microphone 16 and a receiver 24 and that are worn by the user on different sides of the head. FIG. 2 shows just one of the individual devices in simplified fashion.

(8) The signal processing 18 features multiple algorithms 14 that are preferably applied according to the present situation, i.e. depending on the situation, multiple algorithms 14 also being able to be applied simultaneously. For application in a respective situation, each algorithm 14 has an adjustable potency W, as already indicated above. The potency W is e.g. a value of between 0 and 5, the algorithm 14 being inactive at 0, i.e. not producing an effect, and producing a stronger effect as the value increases. Which potency W is used in which situation for a respective algorithm 14 is predefined. The method now attempts to find more optimum potencies W for the algorithms 14 and to adapt the predefined potencies W in suitable fashion.

(9) Each algorithm 14 has at least one assigned signal feature M and the present potency aW of a respective algorithm 14 is set depending on the situation by setting the present potency on the basis of a strength S of the signal feature M in the input signal 12 in the present situation. The processing by the signal processing 18 is accordingly effected on the basis of the respective strength S of specific signal features M in the input signal 12. The hearing device 4 then reacts to the signal features M in a respective situation by applying appropriate algorithms 14 with predetermined potency W, which is then accordingly a present potency aW in a present situation. In the present case, a respective algorithm 14 acts selectively in respect of the associated signal feature M and leaves other components of the input signal 12 as unaltered as possible. A respective signal feature M is boosted or reduced by the associated algorithm 14, for example.

(10) Which algorithms 14 are available and used and which signal features M are sought in the input signal 12 and extracted therefrom is of lesser significance. Examples of algorithms 14 are noise rejection, in order to reject disruptive sounds, e.g. machine or motor sounds, as signal feature M, wind noise rejection, in order to reject wind noise, with microphone noise as signal feature M, feedback rejection, sound smoothing, in order to reject impulses as signal feature M, directionality, i.e. a directional effect of the microphones 16, in order to emphasize sound from a specific direction, compression, specifically frequency compression, and voice recognition, in order to emphasize speech.

(11) The signal processing 18 according to the exemplary embodiment in FIG. 2 operates as follows: predefined signal features M are extracted from the input signal 12. If an applicable signal feature M is present, the associated algorithm 14 is applied in order to process the applicable signal feature M in a specific manner and thereby to emphasize or reject it compared to the rest of the input signal 12, for example. The potency W with which the algorithm 14 is applied, as provided for said purpose in a present situation, is referred to as the present potency aW and is here dependent on the strength S of the signal feature M. The present potency aW is sometimes less than optimum.

(12) The hearing device 4 shown has an extraction unit 26 and a combination unit 28 in addition to the signal processing 18. Starting from the microphone 16 of the hearing device 4, the input signal 12 is routed along a main signal path 30 to the combination unit 28 and, after the latter, to the receiver 24 for output. At the same time, the input signal 12 is routed along a first secondary signal path 32, which branches off from the main signal path 30, to the extraction unit 26, in order to extract signal features M. The extraction unit 26 detects any signal features M present in the input signal 12 and identifies them, so that they can be processed by the signal processing 18 in a specific manner. The extraction unit 26 here also measures the strength S of a respective signal feature M. Additionally, the input signal 12 is routed along a second secondary signal path 34, which likewise branches off from the main signal path 30, to the signal processing 18, for processing. The signal processing 18 is also connected to the extraction unit 26, so that information relating to the signal features M is transmitted from the extraction unit 26 to the signal processing 18, and the signal processing 18 can be controlled, and is controlled, such that the detected signal features M are processed in a specific manner. To that end, the signal processing 18 applies the algorithm 14 that is assigned to a respective signal feature M. As the result, the signal processing 18 outputs a processed signal 36 as an output signal, which is then supplied to the combination unit 28 and is mixed by the latter with the input signal 12 from the main path 30, i.e. the processed signal 36 is applied to the input signal 12. The overall result of this is then an output signal 22 that is output via the receiver 24. As an alternative to this configuration shown in FIG. 2, other configurations and interconnections are also conceivable and suitable.

(13) FIG. 3 shows a flowchart for an exemplary embodiment of a method according to the invention for operating the hearing system 2. The method is effectively used for improved setting of the hearing device 4 and in this respect also for operating the hearing device 4.

(14) The hearing system 2 is configured to recurrently receive a report from the user indicating that the user is dissatisfied with the sound output in the present situation. The receiving, i.e. the receipt, of a report here takes place in a first method step V1 of the method. The dissatisfaction does not need to be explained or specified further by the user, which means that the report is undifferentiated negative feedback. To receive a report from the user, the hearing system 2 has an input element 38, here on the supplementary device 6, alternatively or additionally at another location, e.g. on the hearing device 4. The supplementary device 6 shown here is a mobile terminal, specifically a smartphone. A report can be generated by operating the input element 38.

(15) Furthermore, as can be seen in FIG. 1, the hearing system 2 has a database 10. Said database contains multiple weights G for each algorithm 14, in order to rate a change of the potency W, i.e. in order to rate a possible change in the value of the potency W. Illustrative weights G are indicated in FIGS. 4-6. A respective weight G accordingly links two potencies W to one another, to be more precise two values for the potency W of an algorithm 14, namely the present potency aW to a possible future potency, or in other words an initial potency aW or actual potency to a target potency zW or possible potency. The number of weights G is accordingly dependent on the number of values for the potency W. In the exemplary embodiment shown, 36 weights are then obtained for an algorithm 14 with a potency W adjustable in steps of 1 in the range from 0 to 5. A respective weight G rates the change from the initial potency aW to one of the possible target potencies zW. If the target potency zW is the same as the initial potency aW, the weight G accordingly rates retention of this value. For a single value for the initial potency aW, as many weights G are accordingly obtained as there are possible values for the potency W. These weights G for a specific potency W form a weight profile P or weight vector for this potency W. An illustrative weight profile P is marked in FIG. 6. Multiple weight profiles P then form a two-dimensional weight matrix X, as can be seen in FIGS. 4-6. A respective weight G is a measure of the improvement that can be expected in the sound output if the present potency aW is retained or a different potency W is used, which means that in this respect the weights G are suitable for rating a change of the potency G. The result of rating may be that a change is useful or that retention is more useful. Since a respective weight G therefore indicates how worthwhile the use of the target potency zW instead of the initial potency aW is, the weights G are also referred to as preferences, a weight profile P is referred to as a preference profile and the weight matrix X is referred to as a preference matrix.

(16) If a report is received, each of the algorithms 14 is rated by using the weights G for each of the algorithms 14 to ascertain an individual-case relevance R_e, in order to assess the effect of a change of the potency in the present situation. The individual-case relevance R_e is ascertained by looking it up or calculating it, for example. This rating of the algorithms takes place in a second method step V2 of the method. The report from the user signals that the present setting, which comprises the currently used potencies aW, is unsatisfactory for the user, i.e. the user is dissatisfied with one or more of the currently selected potencies aW for the algorithms 14. Since the information content of the report does not go beyond the mere dissatisfaction and the user currently does not need to provide more precise details regarding the criticized or desired signal processing, it remains unclear to which algorithms 14 and potencies W the dissatisfaction and the report relate. For a respective algorithm 14, it is initially established what present potency aW there is in the present situation. The weight matrix X, more precisely the applicable weight profile P and the weights G thereof, is then used to ascertain how relevant this algorithm 14 is to the dissatisfaction on which the report is based. In principle, the following applies: the more the weights G recommend a different potency W instead of the present potency aW, the more the applicable algorithm 14 appears to be responsible for the dissatisfaction of the user and therefore the more relevant this algorithm 14 is. The individual-case relevance R_e is therefore in particular a measure of the probability of the associated algorithm 14 being set in less-than-optimum fashion for the user. The individual-case relevance R_e does not necessarily have to be calculated as part of the method. Since the individual-case relevance R_e is dependent only on the previously known weights G in the present case, it is possible to calculate all possible individual-case relevances R_e in advance and then to look them up as required during the method.

(17) The method involves multiple individual-case relevances R_e being combined to form a relevance value R for each algorithm 14; the relevance values R are compared with one another, this is taken as a basis for selecting the most relevant algorithm 14, and then an adapted potency pW is used for the latter by adapting the present potency aW of the algorithm 14 for a recommended potency eW determined on the basis of the weights G. The individual-case relevances R_e are each assessments of the probability of an applicable other potency W being likely to have led to a better result and therefore possibly to have prevented a report. In the present case, a respective individual-case relevance R_e is all the greater the more probable it is that another potency W would have led to a satisfactory sound output for the user. The relevance value R is also ascertained as part of the second method step V2. The adaptation of the present potency aW and the use of the adapted potency pW take place in a fourth method step V4 of the method. The determination of the recommended potency eW takes place in the second method step V2 here, since this also involves the weights G being used, but determination at another point is likewise possible and appropriate.

(18) The comparison of the various relevance values R, also referred to as overall ranking, and the selection of the most relevant algorithm 14 take place in a third method step V3 of the method. In order to combine multiple individual-case relevances R_e, an applicable number of reports are received, since each report usually results in precisely one individual-case relevance R_e being ascertained for a respective algorithm 14. These are collected over multiple reports and a relevance value R is calculated for each algorithm 14 from the individual individual-case relevances R_e. The relevance values R of the different algorithms 14 are then compared in an overall ranking in order to find the algorithm 14 that is most relevant and therefore appears most important for the user. In the present case, the algorithm 14 that has the highest relevance value R is selected as the most relevant algorithm 14. In this way, the algorithm 14 that is particularly relevant to the user is identified without the user explicitly needing to provide details in this regard. The more reports are received and utilized, the higher the probability of the setting of the hearing device 4 being adaptable satisfactorily for the user and then also being directly adapted, for example. Since the weights G are already a rating for the different possible changes to another potency W or the retention of the present potency aW, the weights G can also be used to infer a recommendation for a new potency, i.e. a recommended potency eW.

(19) The database 10 in FIG. 1 is in a form such that the strength S of the signal feature M assigned to a respective algorithm 14 is taken into consideration for ascertaining the individual-case relevance R_e and the recommended potency eW. In the present case, the strength S of the respective signal feature M is measured anyway, for example in the extraction unit 26, in order to control the signal processing 18 and to set the potencies W of the algorithms 14 depending on the situation, as already described above. Additionally, in the event of a report, one or more signal features M are now extracted from the input signal 12 and the respective strength S of the signal features is determined. To allow for the strength S of a signal feature M, the database 10 contains multiple weights G for each particular algorithm 14, for different strengths S of the signal feature M, in each case in order to rate a change of the potency W for the ascertained strength S. This can be seen in FIG. 4, which shows a three-dimensional weight matrix X for an individual algorithm 14, with illustrative weights G, a two-dimensional weight matrix X being present as a partial matrix for each strength S of the associated signal feature M. The strength S is mapped to a strength range, e.g. from 0 to 5, 0 meaning that the signal feature M is not present, and the strength S of the signal feature M increasing as the value rises. The weight matrix X for a respective algorithm 14 is therefore not merely two-dimensional, but rather three-dimensional, since the two dimensions of the initial potency aW and the target potency zW are now complemented by a third dimension for the strength S. Accordingly, the number of weights G is also increased. The rating of an individual algorithm 14, i.e. the ascertainment of the individual-case relevance R_e thereof, is now effected on the basis of the strength S ascertained in the present situation for the signal feature M assigned to the algorithm 14.

(20) FIGS. 5 and 6 each show an excerpt from the three-dimensional weight matrix X from FIG. 4. As such, FIG. 5 shows the two-dimensional weight matrix X for a strength S of 5, i.e. a very strong signal feature M, and FIG. 6 shows the two-dimensional weight matrix X for a strength S of 3, i.e. a medium-strength signal feature M. The values shown for the weights G are example values, which, however, clarify the tendency to change to a greater potency W at a greater strength S. FIG. 4 also reveals that the two-dimensional weight matrix X for a strength S of 0, i.e. when the signal feature M is not contained in the input signal 12, is an identity matrix, which means that the applicable weights G indicate that if the signal feature M is not present it is recommended that the present potency aW be retained.

(21) In the exemplary embodiment shown in FIGS. 4-6, a respective weight G indicates what proportion of users in a reference group prefers the associated change. In the present case, a respective weight G is generated by appropriate experiments and recordings in conjunction with other users of hearing devices 4. A respective weight matrix X then contains those proportions of users in the reference group that have changed in each case for a specific strength S of a specific signal feature M and from an initial potency aW to a specific target potency zW (or have possibly retained the initial potency aW). In FIGS. 4-6, the weights G of a respective weight profile P are normalized such that the sum thereof yields 100. A report then results in the database 10 being checked for which potencies W are preferred by the reference group and therefore recommended, as it were, for a respective algorithm 14 when the extracted signal features M are present. The recorded behavior of other users can therefore be taken as a basis for ascertaining the individual-case relevances R_e and a recommended potency eW for another user.

(22) The reference group contains only users who are similar to the user, for example, in particular users for whom a similar audiogram to that for the user was ascertained. The scale used for the similarity of the user with the users in the reference group and the selection thereof is for example the similarity of the audiograms thereof and/or of other individual features, e.g. age, sex, type of hearing deficiency, and the like. In this case it is assumed that similar users also have similar preferences and requirements in regard to the operation of the hearing device.

(23) The recommended potency eW is calculated from the weights G whenever a report is received or once in advance and possibly again when the weights G are updated. In the present case, the recommended potency eW is calculated from the weights G by means of a statistical evaluation, e.g. a mean value formation or a median value formation. This involves using the weights G of the weight profile P for the present potency aW. Assuming a three-dimensional weight matrix X, e.g. as in FIG. 4, the strength S of the signal feature M of the associated algorithm 14 and the present potency aW are taken as a basis for selecting the applicable weight profile P, which contains the various weights G for this strength S and this potency W, as initial potency aW, for selecting a respective target potency zW. By way of example, the strength S is 3, which means that the two-dimensional weight matrix X from FIG. 6 is used. The present potency aW is likewise 3, for example, which means that the marked weight profile P is selected in FIG. 6. These six weights G in conjunction with the possible potencies W are then used to calculate which potency W is recommended, e.g. by means of mean value or median value formation. By way of example, a respective target potency zW is multiplied by the associated weight G and therefore weighted; the target potencies zW weighted in this manner are then added and/or divided by the sum of the weights G, here 100. In the example, the potency W obtained is then 3.42, which is additionally rounded to a recommended potency eW of 3, for example. The recommended potency eW calculated can match the present potency aW, in principle, but the associated algorithm 14 will then be less relevant, since there is a match e.g. with the underlying reference group, of course. If there is a difference between the recommended potency eW and the present potency aW, however, then it can be assumed that a change to the recommended potency eW in the present situation would lead to an improvement. This is the case for example if the present potency aW is 0 in FIG. 6. The recommended potency eW obtained is again 3, which then differs from the initial potency aW 0.

(24) The individual-case relevance R_e is a characteristic quantity for rating an algorithm 14 in the present situation for which the report has been made. The following applies: the greater the individual-case relevance R_e of a first algorithm 14 in comparison with the individual-case relevance R_e of a second algorithm 14, the more relevant the first algorithm 14 appears for the user in the present situation compared with the second algorithm 14. The same also applies to the relevance value R, which is inferred from the individual-case relevance R_e. A respective individual-case relevance R_e is calculated on the basis of the weights G that are stored in the database 10 and in which in particular recommendations and/or experiences of other users and/or experts are coded. In principle, various calculation methods are possible and appropriate individually or in combination.

(25) A first calculation method involves a respective individual-case relevance R_e being calculated on the basis of a potency difference, which is the difference between the present potency aW and the recommended potency eW. In the present case, the absolute value of the difference is also formed, so that a higher individual-case relevance R_e is obtained for greater distance, irrespective of whether the recommended potency eW is above or below the present potency aW. Expressed as a formula, the first calculation method then yields a parameter f1 as follows:
f1=abs (present potency aW−recommended potency eW).

(26) For the aforementioned example with the present potency aW of 3 in FIG. 6, f1=0 is then obtained, provided that the recommended potency eW is rounded. On the other hand, if the present potency aW is 7, for example, then the recommended potency eW obtained from FIG. 6 is likewise 3 and therefore f1=3.

(27) The adapted potency pW used is simply the recommended potency eW, for example. Alternatively, for example an intermediate value is formed, e.g. the mean value from the present potency aW and the recommended potency eW, in order to achieve an adaptation for the recommended potency eW.

(28) A second calculation method involves a respective individual-case relevance R_e being calculated on the basis of a change recommendation, which is a measure of the sum of the weights G for changing to a different potency W, on the one hand, compared with the weight G for retaining the present potency aW, on the other. In the present case, the change recommendation formed is a normalized difference between the sum of the weights G for changing to a different potency W and the weight G for retaining the present potency aW. The weights G of the weight profile P for the present situation and the present potency aW are used in this case. For normalization, this difference is divided by the sum of all weights G of this weight profile P. Expressed as a formula, the second calculation method then yields a parameter f2 as follows:
f2=(sum of all weights G for a change−weight G for retention)/sum of all weights G.

(29) When applied to the weight profile P marked in FIG. 6, by way of illustration, the sum of the weights G for changing to a different potency W accordingly yields 0+0+0+37+1=38. The weight G for retaining the present potency aW is 62. The difference is then 38−62=−24, and, when normalized, f2=−0.24 is then obtained. By contrast, f2=(99−1)/100=0.98 is then obtained from FIG. 6 for a present potency aW of 0.

(30) A third calculation method involves a respective individual-case relevance R_e being calculated on the basis of a measure of scatter of the target potency zW for the present potency aW. The measure of scatter indicates the extent to which the weights G are focused on an individual potency W. The measure of scatter is for example a variance of the target potencies zW. If the weights G simply each indicate a number of users, a weight G pertaining to a specific data pair containing initial potency aW and target potency zW simply yields the number of data points for this data pair. These data points are then statistically evaluated. It is possible to read from the measure of scatter how strongly a specific potency W is recommended or whether multiple potencies W are possible, ultimately that is to say how pronounced the recommendation on the basis of the database 10 is. The higher a respective weight G, the more data points recommend the associated target potency zW. The measure of scatter is inverted in the present case, which means that a low measure of scatter yields a high individual-case relevance R_e and therefore makes an algorithm 14 appear all the more relevant. A suitable formula for the third calculation method, which then yields a parameter f3, is as follows:
f3=exp (1/exp(sqr(V))),
where “exp” denotes the exponential function with base e, “sqr” is a square root and “V” is a variance of the target potency zW of the relevant weight profile P and is calculated as follows, for example:
V=(1/n)*sum(x_i−M(x)){circumflex over ( )}2,
where x_i are the target potencies zW and M(x) is a mean value or median of the potency W, i.e. target potency zW here, and where all data points of the weight profile P are used for summation. In the example in FIG. 4-6, M(x) is e.g. the mean value of the potencies W and is then 2.5. The weight profile P is formed from 100 data points in accordance with the sum of the weights, i.e. n=100. In FIG. 6, the data pair (initial potency aW=3; target potency zW=3) occurs, by way of illustration, 62 times for the marked weight profile P of the initial potency 3, that is to say that 62 data points (3; 3) are present. This results in a variance V=1.05 and accordingly f3=1.43. By contrast, V=0.29 and f3=1.79 is accordingly obtained for the initial potency 0 in FIG. 6, that is to say a lower measure of scatter and hence a higher individual-case relevance R_e.

(31) In the present case, the three aforementioned calibration methods are combined by multiplying the parameters f1, f2, f3 in order to obtain the individual-case relevance R_e:
R_e=f1*f2*f3.

(32) This is carried out for each of the algorithms 14, as a result of which an individual-case relevance R_e is ascertained for each algorithm 14 for the present situation.

(33) The relevance value R of a respective algorithm 14 is likewise calculated from the individual-case relevances R_e of this algorithm 14 by means of a statistical evaluation, e.g. a median value formation. Typically, higher individual-case relevances R_e also yield a higher relevance value R.

(34) From the above, it becomes clear that a single report is typically not sufficient to identify and adapt one of the algorithms 14 as the most relevant algorithm 14 with satisfactory probability. In one configuration, the present potency aW of the most relevant algorithm 14 is thus adapted for the recommended potency eW only when the relevance value R of the most relevant algorithm 14 differs from the relevance values R of the other algorithms 14 by at least a minimum value dR. Accordingly, a period of time is waited until a distinction defined as sufficient above the minimum value dR is reached and one of the algorithms 14 is distinguished with sufficient certainty from the other algorithms 14. The minimum value dR is for example a minimum required difference between the highest relevance value R and the next highest relevance value R.

(35) Moreover, the weights G in the database 10 are optionally updated on the basis of the adapted potency aW and the adapted potency aW is therefore taken into consideration from then on for ascertaining an individual-case relevance R_e and a recommended potency eW. The database 10 is therefore continually updated.

(36) The adapted potency pW is used as the new, present potency aW from then on, and so the adapted potency pW is automatically used when the present situation arises again. The adapted potency pW is thus set directly by the hearing system 2 and is then the potency W that is used in future when an applicable situation arises. Should a report then still be made again, the method is continued as already described in order to obtain a further adaptation thereof or of another algorithm 14. As an alternative to the direct application of the adapted potency pW, the adapted potency is proposed to the user in a test mode initially and is used as the new present potency aW only after a confirmation by the user. The test mode is therefore used for test hearing, as it were, and the user is provided with the opportunity to test the adapted potency pW beforehand and then either to accept it or to reject it. This is made possible by way of appropriate input elements 38, e.g. on the hearing device or on the supplementary device 6.

(37) In order to prevent possible constriction of the data in the database 10, optionally a different, experimental potency W is occasionally proposed in the test mode instead of an adapted potency pW, that is to say that the user is deliberately not offered the potency pW adapted on the basis of the method, but rather is intentionally offered a different and possibly less optimum potency W. If the experimental potency W is then nevertheless satisfactory for the user, he will accept the experimental potency W, and so said potency is used as the new present potency aW by the hearing system 2 from then on. The weights G in the database 10 are also updated on the basis of the experimental potency W, and said potency is therefore taken into consideration from then on for ascertaining an individual-case relevance R_e and a recommended potency eW. The experimental potency W is chosen to be higher or lower than the recommended potency eW, for example, or is a random value.

(38) As shown in FIG. 1, the hearing system 2 contains at least one hearing device 4 and a database 10 as described above. The hearing device 4 is connected to the database 10 by way of a data connection 40, e.g. via the Internet, for the purpose of data interchange. The database 10 is a part of the server 8 here, which is accordingly a part of the hearing system 2. Furthermore, the hearing system 2 in the exemplary embodiment shown here also contains the supplementary device 6, which is used as a mediator between the hearing device 4 and the server 8 and to connect these for the purpose of data interchange. The hearing device 4 and the supplementary device 6 are connected by way of a Bluetooth connection, for example, for the purpose of data interchange, whereas the supplementary device 6 and the database 10 are connected via the Internet, which is not explicitly denoted, as shown in FIG. 1, for example.

(39) In the exemplary embodiment shown, the calculation of the individual-case relevances R_e takes place on the server 8, but this is not imperative. By contrast, the calculation of the relevance values R here takes place on the supplementary device 6, which is likewise not imperative, however.

(40) The following is a summary list of reference numerals and the corresponding structure used in the above description of the invention: 2 hearing system 4 hearing device 6 supplementary device 8 server 10 database 12 input signal 14 algorithm 16 microphone 18 signal processing 20 control unit 22 output signal 24 receiver 26 extraction unit 28 combination unit 30 main signal path 32 first secondary signal path 34 second secondary signal path 36 processed signal 38 input element 40 data connection aW present potency, initial potency dR minimum value eW recommended potency G weight M signal feature P weight profile pW adapted potency R_e individual-case relevance S strength of the signal feature V1 first method step V2 second method step V3 third method step V4 fourth method step W potency X weight matrix zW target potency