Hierarchical environmental classification in a hearing prosthesis

Patent metadata

Field	Value
Publication number	US-12273685-B2
Application number	US-202318318978-A
Country	US
Kind code	B2
Filing date	May 17, 2023
Priority date	Oct 17, 2017
Publication date	Apr 8, 2025
Grant date	Apr 8, 2025

Abstract

Presented herein are techniques for generating a hierarchical classification of a set of sound signals received at hearing prosthesis. The hierarchical classification includes a plurality of nested classifications of a sound environment associated with the set of sound signals received at hearing prosthesis, including a primary classification and one or more secondary classifications that each represent different characteristics of the sound environment. The primary classification represents a basic categorization of the sound environment, while the secondary classifications define sub-categories/refinements of the associated primary classification and/or other secondary classifications.

First claim

What is claimed is:

1. A hearing device, comprising:
one or more input elements configured to receive a plurality of sets of sound signals over a period of time;
a memory; and
one or more processors coupled to the memory and to the one or more input elements, wherein the one or more processors are configured to:
process the plurality of sets of sound signals with a sound processing path to generate output signals for use in delivering stimulation signals to a recipient of the hearing device;
perform an environmental classification of a sound environment associated with each of a number of the plurality of sets of sound signals, wherein each environmental classification results in a classification output that represents the environmental classification for a corresponding set of sound signals; and
store each of the classification outputs in the memory to form a classification data set.

2. The hearing device of claim 1 , wherein the one or more processors are configured to:
analyze the classification data set; and
automatically adjust one or more settings of the sound processing path based on the analyzing of the classification data set.

3. The hearing device of claim 2 , wherein the one or more settings comprise at least one of: noise reduction settings, tinnitus masking settings, microphone settings, gain settings, channel dynamic range, maxima selection, or comfort settings.

4. The hearing device of claim 2 , wherein analyzing the classification data set comprises:
analyzing the classification data set with a machine learning algorithm.

5. The hearing device of claim 1 , wherein at least one environmental classification of at least one of the plurality of sets of sound signals includes a hierarchical classification that includes a primary classification and a secondary classification, the secondary classification sub-categorizing the primary classification.

6. The hearing device of claim 5 , wherein the primary classification indicates that the sound environment is an environment that includes speech, and wherein the secondary classification sub-categorizes the sound environment based on attributes of the speech.

7. The hearing device of claim 5 , wherein the primary classification indicates that the sound environment is an environment that includes noise, and wherein the secondary classification sub-categorizes a type of noise in the sound environment.

8. The hearing device of claim 5 , wherein the primary classification is a classification of at least one of speech or speech-in-noise, and wherein the secondary classification is at least one of own voice or external voice.

9. A non-transitory computer media comprising instructions that, when executed by one or more processors, are configured to cause the one or more processors to perform operations comprising:
processing a plurality of sets of sound signals received at a hearing device to generate output signals for use in delivering stimulation signals to a recipient of the hearing device;
performing an environmental classification of a sound environment associated with each of a number of the plurality of sets of sound signals, wherein each environmental classification results in a classification output that represents the environmental classification for a corresponding set of sound signals; and
storing each of the classification outputs to form a classification data set.

10. The non-transitory computer media of claim 9 , wherein performing the environmental classification of the sound environment generates a primary classification and a secondary classification, the secondary classification sub-categorizing the primary classification.

11. The non-transitory computer media of claim 10 , wherein the operations comprise:
conditionally generating the secondary classification based on an attribute of the primary classification.

12. The non-transitory computer media of claim 9 , wherein the operations comprise:
analyzing the classification data set; and
automatically adjusting one or more settings of a sound processing path used to generate the output signals based on the analyzing of the classification data set.

13. A method, comprising:
receiving a plurality of sets of sound signals at a hearing device over a period of time;
processing the plurality of sets of sound signals with a sound processing path to generate output signals for use in delivering stimulation signals to a recipient of the hearing device;
performing an environmental classification of a sound environment associated with each of a number of the plurality of sets of sound signals, wherein each environmental classification results in a classification output that represents the environmental classification for a corresponding set of sound signals; and
storing each of the classification outputs in memory to form a classification data set.

14. The method of claim 13 , further comprising:
analyzing the classification data set; and
automatically adjusting one or more settings of the sound processing path based on the analyzing of the classification data set.

15. The method of claim 14 , wherein the one or more settings comprise at least one of: noise reduction settings, tinnitus masking settings, microphone settings, gain settings, channel dynamic range, maxima selection, or comfort settings.

16. The method of claim 14 , wherein analyzing the classification data set comprises:
analyzing the classification data set with a machine learning algorithm.

17. The method of claim 13 , wherein at least one environmental classification of at least one of the plurality of sets of sound signals includes a hierarchical classification that includes a primary classification and a secondary classification, wherein the secondary classification sub-categorizes the primary classification.

18. The method of claim 17 , wherein the primary classification indicates that the sound environment is an environment that includes speech, and wherein the secondary classification sub-categorizes the sound environment based on attributes of the speech.

19. The method of claim 17 , wherein the primary classification indicates that the sound environment is an environment that includes noise, and wherein the secondary classification sub-categorizes a type of noise in the sound environment.

20. The method of claim 17 , wherein the primary classification is a classification of at least one of speech or speech-in-noise, and wherein the secondary classification is at least one of own voice or external voice.

Assignees

• Cochlear Ltd

Inventors

• Goorevich Michael
• Qazi Obaid Ur Rehman
• Puchner Carl

Classifications

• H04R2225/41

  Detection or adaptation of hearing aid parameters or programs to listening situation, e.g. pub, forest · CPC title

• H04R25/75

  Electric tinnitus maskers providing an auditory perception · CPC title

• A61N1/36036

  of the outer, middle or inner ear · CPC title

• H04R2225/43

  Signal processing in hearing aids to enhance the speech intelligibility · CPC title

• H04R25/50 Primary

  Customised settings for obtaining desired overall acoustical characteristics · CPC title