Hearing Aid Unsupervised Learning Acoustic Adaptation
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Solution Overview
Problem
Existing hearing aids do not fully adapt to individual users' needs by learning from both user interactions and acoustic environments, resulting in static learning features that do not provide a complete picture of sound changes.
Innovation Solution
A hearing aid with an unsupervised learning capability that includes a signal processing unit to categorize and adapt to acoustic environments, using input and output units to convert sound into electric signals, and a memory unit to store and adjust control parameters based on user interactions and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hearing aids use traditional data logging to record user interactions, then user adaptation is improved, but the learning system remains static and cannot capture complete acoustic environment information
Solution Approach 1:
The hearing aid system performs multiple functions: it logs user interactions through the user interface, simultaneously records acoustic environment data through the signal processing unit, and integrates both data streams in the memory unit. This multi-functional approach allows the system to capture both user behavior and environmental context, resolving the information loss problem while maintaining adaptability.
Solution Approach 2:
The system implements feedback by continuously monitoring user interactions and acoustic environments, storing this data in memory, and using the accumulated information to dynamically adjust signal processing parameters. The learning controller uses this feedback loop to progressively improve the hearing aid's adaptation to individual users while maintaining awareness of acoustic contexts.
2Ease of operation
If hearing aids implement learning features based on user interactions, then program selection is optimized, but the system cannot distinguish between different acoustic environments causing the interactions
Solution Approach 1:
The system adds a new dimension of data collection by recording acoustic environment parameters alongside user interaction data. Instead of only logging program selection changes, the system now captures multi-dimensional information including acoustic characteristics, sound pressure levels, and environmental context, enabling more sophisticated analysis and adaptation.
Solution Approach 2:
The hearing aid performs preliminary recording of acoustic environments continuously in the background before user interactions occur. This preliminary data collection ensures that when a user makes a program selection change, the system already has contextual acoustic information available for immediate analysis and future adaptation.
3Adaptability or versatility
If hearing aids continuously monitor and learn from acoustic environments, then adaptation to individual needs is improved, but device complexity increases
Solution Approach 1:
The system merges the data logging functionality with the existing signal processing unit and memory. Rather than adding completely separate monitoring hardware, the invention integrates environmental sensing and data recording into the existing signal processing chain, sharing computational resources and data pathways to minimize additional complexity.
Solution Approach 2:
The hearing aid performs self-monitoring and self-adjustment using its existing sensors and processing capabilities. The system automatically records acoustic environments, analyzes user interactions, and adjusts signal processing parameters without requiring external intervention or additional complex control systems, enabling adaptation through self-service mechanisms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The hearing aid continuously adapts to individual user needs, improving sound processing and user experience across various acoustic environments, providing better sound pressure management and program selection optimization.
Implementation Method 1
an input unit adapted to convert an acoustic environment to an electric signal
Implementation Method 2
an output unit adapted to convert an processed electric signal to a sound pressure
Data Source
AI summary
The present invention relates to a hearing aid logging data and learning from these data. The hearing aid (10, 100) comprises an input unit (12) converting an acoustic environment to an electric signal; an output unit (16) converting an processed electric signal to a sound pressure; a signal processing unit (14) interconnecting the input and output unit, and generating the processed electric signal from the electric signal according to a setting; a user interface (18) converting user interaction to a control signal thereby controlling the setting; and finally a memory unit (20) comprising a control section storing a set of control parameters associated with the acoustic environment, and a data logger section receiving data from the input unit (12), the signal processing unit (14), and the user interface (18); and wherein said signal processing unit (14) configures the setting according to the set of control parameters and comprises a learning controller adapted to adjust the set of control parameters according to the data in the data logging section.