Note: Descriptions are shown in the official language in which they were submitted.
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Learning by provocation
The present invention refers to a method for the adjustment
of settings of a hearing aid according the introduction of
claim 1 and to a hearing aid comprising software to
implement a method for the adjustment of settings of the
aid.
So called self-learning hearing aids are known, where the
adaptation of optimized settings is automatically executed
by the hearing aid itself.
A drawback or problem exists in recognising valid or true
modifications made by the user.
No modifications of the settings for a long period does not
explicitly means, that the user is happy with the
respective settings. It might well be, that the user is not
familiar with the manipulation of settings of the hearing
aid or the settings are such, that the user can live with
the settings but they are not optimized.
Today's high end hearing instruments incorporate
sophisticated schemes to automatically adjust the
instrument parameters to specific acoustic environments.
They hereby provide optimized sound qualities and speech
perception in all situations. The current techniques have
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still some drawbacks in terms of fulfilling individual
needs and preferences of the hearing instrument users, as
mentioned above. In order to get more insight to these
individual requirements data logging has become an
interesting tool while reporting all the users'
interactions with the hearing instruments to the fitter.
There are existing hearing aids, which can automatically
analyse the data log stored in the non-volatile memory of
the hearing instrument and provide some changes to the
current settings. The fitter can either accept the proposed
modifications or make changes him/herself. Most of the
times these modifications yield to an improved comfort for
the hearing instrument users since interactions with the
hearing instrument tend be needed less often than prior to
the modified adjustments.
It is a disadvantage of the current actual solutions that
modifications have to be done either by the fitter or
audiologist since the user can't neither reprogram the
hearing aid himself/herself nor allocate the hearing
instrument to update its setting based on frequent user
interactions. To overcome these shortcomings the hearing
instrument should learn out of user interactions and
optimize settings automatically, "User preference learning"
has yet been developed. Data logging is still the basic
tool for the procedure; learning algorithms will exploit
the data gathered over time within different acoustical
environments. The results are now interpreted in the
hearing instrument and directly applied, a visit of the
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fitter or audiologist is no more needed and this is a great
advantage.
This improved method still has some drawbacks; the
performance and validity of the embedded learning rules
depends to a large extend on user interactions. The more
interactions there are the faster and better learning
converges. A couple of single interactions would not really
train the system efficiently. Since hearing instruments
incorporate different programs, training has to be done for
all accordingly. It might therefore take long until the
user gets a real benefit out of his/her self-learning
hearing instrument and this must be overcome.
In addition many changes in settings made by the user does
not automatically mean, that the initial settings were bad.
Vice versa as stated above no changes in settings does not
automatically means, that the settings are good.
Several ways to intensivate and shorten the
learning/training process of an intelligent hearing
instrument can be described:
- A special acoustical training parcour could be
defined, which would present a large variety of significant
real life situations to a hearing instrument user, while
he/she is continuously adjusting the hearing instrument
accordingly. Such a training parcour could be provided on a
CD, MP3 file/player or alike. In a couple of minutes/hours
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the hearing instrument would be trained instead of
weeks/months and hereby individually optimized much faster.
Although a lot of realistic acoustical sounds and
environments could be played through by the mentioned
procedures, differences between the simulated and fully
natural situations would remain. The quality of the
respective sound presentation via loudspeaker will
influence the outcome and validity of the training
sequence.
- Training in the real worid is much preferred.
It is therefore an object of the present invention to
describe a method applicable in the real world, still
shorten the learning time and increase the amount of user
interactions to the level needed to reliably estimate
optimal individual settings of the crucial parameters.
It is a further object of the present invention to propose
a solution or method respectively for an improved
adjustment of settings of a hearing aid or hearing
instrument respectively by using a increased amount of
setting changes initiated by the user due to non optimal
settings of the hearing aid giving the user the possibility
for improved adjustment without the need of consulting an
audiologist or fitter respectively.
It is furthermore an object of the present invention to
provide a hearing aid or hearing instrument respectively
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suitable for improved adjustments of hearing instrument
settings by learning algorithms where optimal adjustments
can be achieved within shortened period.
5 According the present invention a method according the
wording of claim 1 is proposed.
According the inventive method for the improved adjustments
of settings of a hearing instrument or hearing aid
respectively it is proposed, that at least one setting of
the hearing aid for a particular acoustic environment is
changed or deviated from the actual setting without any
preceding action or manipulation of the user to provoke the
user to interact or readjust e.g. said setting.
This provocation could be a change in volume, output level,
spectral shape, distortions (feedback-canceller), noise
cleaning (noise canceller, beamformer), program or any
other significant alteration within the actual acoustical
environment. If the user would not interfere, the change
would not be significant, thus informative for the learning
sequence as well. On the other hand it might well be, that
the user was not aware about the deviation or was not in a
position to react within a reasonable time period. With
other words it might well be, that the deviation from the
actual setting has to be repeated to again provoke an
interaction by the user.
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The repetition of the provocation can be either an
additional deviation or a repetition of the original
deviation, which means that before repetition of the
provocation the settings will be reset.
The provoked interaction of the end-user either can e.g. be
a change on exactly the same parameter the hearing aid has
changed or can consist in simply accepting or declining the
change. In the latter case the scope of parameters on which
provocation learning can be applied is much broader than in
the first case, because it is not necessary that the
hearing aid's end-user interface offers direct access to
the changed parameter.
The user could be informed about the special behaviour of
the hearing instrument so that he/she could stop the
procedure in case of serious annoyance. However a blind
experiment could be made as well, what ever is the more
appropriate approach in praxis.
The provocation could be randomly out of the box or
following some rules or templates which means the deviation
or changes of the at least one setting for a particular
acoustical environment could be changed randomly or
according to a predetermined regular or irregular rule,
algorithm, etc. The changes or deviations in settings may
be depending on user responses or data memorized in the
meantime of the learning period. Provocations strategies
and rules can be derived from various tests of different
user persons and using different algorithms, programs, etc.
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according which the deviations or changes of the settings
of the hearing instrument are initiated.
For the adjustment of the settings of a hearing instrument
at least one setting can be repeatedly changed or deviated
from the proceeding settings for a particular acoustic
environment and respective repeated interactions or
readjustments done by the user can lead to a final
optimized setting value, which can be stored within the
hearing instrument as new basic optimized setting of the
hearing aid for the mentioned particular acoustic
environment.
According a further method it is possible, that after a
change or deviation from the actual setting for a
particular acoustic environment in case no interaction or
readjustment is done by the user or is recognized by the
hearing instrument it might by advisable to either repeat
the deviation or change of the setting and/or to inform the
user e.g. acoustically about the non recognized change of
the settings.
Again furthermore it is possible that within the hearing
instrument so called basic settings are stored which will
remain unchanged while a so called actualized setting value
for a particular acoustic environment is changed to provoke
the user to interact and to readjust the user- setting
while the basic device setting remains unchanged. Only if
the user person is of the opinion, that the actualized
setting or user setting is optimal the basic setting of the
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hearing instrument will be changed or adjusted
respectively. It is further possible that the basic hearing
instrument settings will be changed or adjusted only after
restart of the hearing instrument. Therefore at least some
of the individual settings of the hearing instrument each
for a particular acoustic environment may comprise a basic
setting value and an actualized setting value which latter
is changed without the influence of the user to provoke the
user to interact or adjust the respective user setting, the
respective basic setting of the hearing instrument is only
adjusted to the respective user setting upon activation by
the user, an audiologist, a fitter and/or at restart of the
device.
Again according a further proposed method the user could be
informed about the change or deviation from the actual
setting after a certain period, first of all asking the
user, whether he recognized the change and if yes, if in
case of a change or readjustment of the user setting the
actual setting is improved, equivalent or worth compared
with the initial setting.
One problem of course may occur, if the acoustic
environment conditions change rapidly, so that one and the
same setting can not be changed within a reasonable time
period for a particular acoustic environment. It is
therefore preferred, that changes of settings or deviations
from actual settings will only be initiated in case, that
the user will stay in more or less constant acoustic
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environments. Otherwise in case of rapid changes of
acoustic environment any randomly initiated changes in
settings should be neglected or reset to the initial
settings.
The above mentioned and proposed inventive methods in
principal are not only suitable for learning sequences
within the instrument but they could be used to speed up
the validation phase while triggering the user to actively
interfere with the instrument and search for the best
program or setting of the instrument in a given solution.
Furthermore according the present invention a respective
software is proposed which enables a hearing instrument to
apply the above mentioned method for improved adjustment of
hearing aid settings for a particular acoustic environment.
Preferred of course is a software which is applicable
universally in most of the today used hearing aids or
hearing instruments respectively at least for some of the
settings used within a hearing instrument.
It is of course possible to incorporate such a software
within the hearing instrument itself or within a remote
control, which is installed e.g. within an ordinary tool
daily used such as e.g. within a arm watch, a handy etc.
