Ear-Worn Acoustic Adaptation for Muffled Speech Intelligibility
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Solution Overview
Problem
Existing hearing devices struggle to optimally configure parameters for various acoustic environments and listening intents, requiring users to manually cycle through memory settings or rely on external device connections, which can be cumbersome and inefficient.
Innovation Solution
An ear-worn electronic device with a processor and non-volatile memory storing multiple parameter value sets for different acoustic environments, allowing users to automatically or semi-automatically adjust settings via a user-actuatable control or sensor inputs, enhancing speech intelligibility in dynamic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually cycle through memory settings to configure hearing devices for different acoustic environments, then the device can be adjusted to match specific listening conditions, but the operation becomes cumbersome and time-consuming
Solution Approach 1:
The hearing device automatically classifies the acoustic environment using microphone inputs and processor-based classification algorithms, then selects and applies appropriate parameter value sets without user intervention. This self-service mechanism eliminates the need for users to manually cycle through memory settings while maintaining adaptability to different acoustic conditions.
Solution Approach 2:
The system stores multiple parameter value sets in non-volatile memory, each associated with different acoustic environments. The processor dynamically changes operational parameters by selecting the appropriate parameter set based on environmental classification, enabling automatic adaptation without manual user configuration.
2Adaptability or versatility
If users connect external devices to adjust hearing device settings, then configuration options expand, but the process becomes more complex and less efficient
Solution Approach 1:
The hearing device performs acoustic environment classification and parameter selection autonomously using its own microphone and processor resources. This self-sufficient operation eliminates the need for external device connections, reducing system complexity while maintaining configuration flexibility through multiple stored parameter sets.
Solution Approach 2:
The processor serves multiple functions: it classifies acoustic environments, selects appropriate parameter sets, and applies them automatically. This multi-functionality consolidates what would otherwise require separate external devices, reducing overall system complexity while preserving adaptability.
3Reliability
If hearing devices continuously monitor and automatically adjust parameters, then speech intelligibility improves in dynamic conditions, but energy consumption increases
Solution Approach 1:
The system performs acoustic environment classification at periodic intervals or triggered by user input rather than continuously. This periodic operation maintains speech intelligibility by updating parameters when environmental changes occur, while reducing energy consumption by keeping the processor in lower-power states between classification events.
Solution Approach 2:
The system uses feedback from acoustic classification to trigger parameter adjustments only when environmental changes are detected. This feedback-driven approach maintains reliability by adapting to actual conditions while conserving energy by avoiding unnecessary processor operations in stable environments.
Data Source
Figure 1A~1B
Figure 1C
Figure 1D
AI summary
An ear-worn electronic device comprises a microphone arrangement configured to sense sound in an acoustic environment, an acoustic transducer, and a non-volatile memory configured to store parameter value sets each associated with a different acoustic environment, at least one of which is associated with an acoustic environment with muffled speech. A control input of the device is configured to receive a control input signal produced by a user-actuatable control, a sensor or an external electronic device. A processor is configured to classify the acoustic environment as one with muffled speech using the sensed sound and, in response to a signal received from the control input, apply one or more of the parameter value sets appropriate for the classification to enhance intelligibility of muffled speech.