Ear-Worn Acoustic Adaptation Using Intent-Based Parameter Sets
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Solution Overview
Problem
Existing hearing devices struggle to optimally configure parameters for various acoustic environments and listening intents without requiring manual memory cycling or constant automatic adjustments, which can be distracting and dependent on device connectivity.
Innovation Solution
An ear-worn electronic device with a processor that classifies the acoustic environment and applies appropriate parameter sets in response to user input or sensor signals, allowing for optimal parameter settings without constant adjustments or device connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual memory cycling is used to configure parameters for different acoustic environments, then the device allows user control over parameter settings, but the operation becomes complex and time-consuming
Solution Approach 1:
The device automatically classifies the acoustic environment using microphone input and sensor data, then autonomously selects and applies the appropriate parameter value set from stored configurations. This self-service mechanism eliminates the need for manual memory cycling while maintaining optimal parameter settings for the detected environment.
Solution Approach 2:
The system stores multiple parameter value sets corresponding to different acoustic environments and automatically transitions between them based on environment classification. This parameter switching approach enables rapid adaptation to different listening conditions without manual intervention or time-consuming cycling through memory options.
2Adaptability or versatility
If constant automatic adjustments are implemented to adapt to different acoustic environments, then the device maintains optimal parameter settings, but the constant changes become distracting to the user
Solution Approach 1:
Multiple parameter value sets are pre-configured and stored in the device for different acoustic environments. The classification system identifies the current environment and retrieves the appropriate pre-prepared parameter set, enabling smooth transitions without constant adjustments. This preliminary preparation reduces unnecessary changes while maintaining adaptability.
3Extent of automation
If automatic acoustic environment classification is implemented, then the device adapts parameters automatically, but the device complexity increases
Solution Approach 1:
The processor performs multiple functions: it classifies acoustic environments, determines wearer activity status, integrates sensor data, and selects appropriate parameter sets. This multi-functionality consolidates what could be separate complex systems into a unified processing approach, managing automation while controlling overall device complexity.
Solution Approach 2:
The system combines acoustic classification with activity status determination and sensor data integration into a unified parameter selection process. By merging these functions, the device achieves comprehensive automatic adaptation without the complexity of separate independent systems for each function.
4Adaptability or versatility
If multiple parameter value sets are stored for different acoustic environments, then the device can provide optimal settings for various conditions, but the memory requirements and device complexity increase
Solution Approach 1:
The device stores multiple parameter value sets with different configurations for various acoustic environments. The classification system identifies the current environment type and retrieves the corresponding parameter set, enabling optimal performance across different conditions. This parameter-based approach provides versatility while managing memory requirements through structured organization of parameter sets.
Data Source
AI summary
An ear-worn electronic device comprises a microphone configured to sense sound in an acoustic environment, an acoustic transducer, and a non-volatile memory configured to store a plurality of parameter value sets, each of the parameter value sets associated with a different acoustic environment. A control input is configured to receive a control input signal produced by at least one of a user-actuatable control of the ear-worn electronic device and an external electronic device communicatively coupled to the ear-worn electronic device in response to a user action. A processor is configured to classify the acoustic environment using the sensed sound and determine a listening intent preference of the user. The processor is configured to apply, in response to the control input signal, one of the parameter value sets appropriate for the classification and the listening intent preference of the user.