Granted Patent B2

US 12,363,487 · App. 17/881,077 · Granted Jul 15, 2025

Inventors: Meng Guo (Smørum, DK); Jesper Jensen (Smørum, DK)

Assignee: Oticon A/S

H04R25/453

Abstract

Claims (60)

1. A hearing aid adapted for being worn by a user at or in an ear of the user, the hearing aid comprising

at least one input transducer for converting sound in an environment around the user to at least one electric input signal representing said sound;

an output transducer for converting an output signal provided in dependence of said least one electric input signal to stimuli perceivable to the user as sound;

a feedback control system configured

to minimize feedback from said output transducer to said at least one input transducer, and

to at least provide a feedback corrected version of said at least one electric input signal; and

an audio signal processor configured

to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal, and

to provide a processed signal in dependence thereof;

wherein the feedback control system is based on a machine learning model receiving input data at least representing

said at least one electric input signal; and

said processed signal;

wherein the feedback control system is configured to provide said feedback corrected version of the at least one electric input signal as an output; and

wherein the machine learning model is trained with synthetic input data, at least some of the synthetic input data having been generated by computer simulation, the synthetic input data at least representing

an external part of said at least one electric input signal;

an feedback part of said at least one electric input signal; and

said processed signal; and

with synthetic output data at least representing

said feedback corrected version of the at least one electric input signal.

2. A hearing aid according to claim 1 wherein said feedback control system is configured to provide said output signal as a further output.

3. A hearing aid according to claim 1 wherein said machine learning model is configured to receive further input data representing information about said one or more processing algorithms.

4. A hearing aid according to claim 1 wherein said feedback control system is configured to provide a control input signal to the audio signal processor as a further output, said control input signal comprising parameters providing inputs to said one or more processing algorithms.

5. A hearing aid according to claim 1 wherein said machine learning model is trained with input data at least representing

said at least one electric input signal; and

said processed signal.

6. A hearing aid according to claim 5 wherein said machine learning model is trained with further input data representing information about said one or more processing algorithms.

7. A hearing aid according to claim 1 wherein said processed signal from the processor provides said output signal.

8. A hearing aid according to claim 1 being constituted by or comprising an air-conduction type hearing aid, a bone-conduction type hearing aid, or a combination thereof.

9. A hearing aid according to claim 1 comprising at least one analysis filter bank for providing said at least one electric input signal in a time-frequency domain representation.

10. A hearing aid according to claim 9 wherein the input data to the machine learning model are

said at least one electric input signal; and

said processed signal,

which for each time index/each are arranged as a vector with K elements, K being the number of frequency bands in the time-frequency domain representation (k,l).

11. A hearing aid according to claim 1 wherein the output transducer comprises a) a loudspeaker for providing said stimuli as an acoustic signal to the user, or b) a vibrator for providing said stimuli as mechanical vibration of a skull bone to the user.

12. A method of training a machine learning model for use in a feedback control system of a hearing aid, the hearing aid comprising

at least one input transducer for converting input sound in an environment around the user to at least one electric input signal representing said input sound;

an output transducer for converting an output signal provided in dependence of said at least one electric input signal to stimuli perceivable to the user as sound;

wherein said input sound comprises an external sound and a feedback sound generated by said output transducer and leaked to said input transducer via feedback path, and wherein said at least one electric input signal likewise comprises an external part originating from said external sound and a feedback part originating from said feedback sound;

a feedback control system for minimizing said feedback part of said at least one electric input signal and at least providing a feedback corrected version of said at least one electric input signal, the feedback control system comprising said machine learning model; and

an audio signal processor configured to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal and to provide a processed signal in dependence thereof;

wherein the machine learning model is trained with synthetic input data, at least some of the synthetic input data having been generated by computer simulation, the synthetic input data at least representing

said external part of said at least one electric input signal;

said feedback part of said at least one electric input signal; and

said processed signal; and

with synthetic output data at least representing

said feedback corrected version of the at least one electric input signal.

13. A method according to claim 12 wherein said synthetic output data further represents said output signal.

14. A method according to claim 12 wherein said synthetic input data further represents information about said one or more processing algorithms.

15. A method according to claim 12 wherein said synthetic output data further represents parameters providing inputs to said one or more processing algorithms.

16. A method according to claim 12 wherein at least said synthetic output data are generated by computer simulation.

17. A method according to claim 12 wherein at least said synthetic output data are generated by computer simulation to reflect an imaginary feedback control system reacting instantly and accurately to feedback changes.

18. A method according to claim 12 wherein an imaginary feedback control system is used to generate data for the training of the machine learning model, both in static feedback situations and with dynamic feedback path changes.

19. A method according to claim 12 wherein the input signals for the training of the machine learning model comprise white noise, or speech, or music signals, or a mixture thereof.

20. A hearing aid comprising

at least one input transducer for converting input sound in an environment around the user to at least one electric input signal representing said input sound;

an output transducer for converting an output signal provided in dependence of said at least one electric input signal to stimuli perceivable to the user as sound;

wherein said input sound comprises an external sound and a feedback sound generated by said output transducer and leaked to said input transducer via feedback path, and wherein said at least one electric input signal likewise comprises an external part originating from said external sound and a feedback part originating from said feedback sound;

a feedback control system for minimizing said feedback part of said at least one electric input signal and at least providing a feedback corrected version of said at least one electric input signal, the feedback control system comprising said machine learning model; and

an audio signal processor configured to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal and to provide a processed signal in dependence thereof;

wherein the machine learning model is trained according to the method of claim 12 .

Assignments (1)

Priority Claims (1)

Continuity (1)

References Cited (13)