Hearing Algorithm Situated Design via Real-Time Parameter Updates
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Solution Overview
Problem
Current hearing algorithm design methods are limited in adapting to real-world scenarios, leading to user dissatisfaction due to unforeseen issues, such as amplified sharp sounds during everyday use of hearing devices.
Innovation Solution
A method and system for situated design of hearing algorithms, utilizing an accessory device with a processor, memory, and wireless interface to continuously update hearing device parameters in real-time based on user feedback and operating data, employing a generative probabilistic model to adapt filter coefficients, noise cancellation parameters, and compressor gains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hearing device parameters are fixed during design, then device complexity is reduced, but adaptability to different real-world scenarios deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by enabling the hearing device to continuously learn and update its parameters in real-time based on user feedback and environmental conditions. The system transitions from static pre-programmed algorithms to dynamic self-adjusting algorithms that adapt to varying real-world scenarios, resolving the contradiction between fixed design simplicity and adaptive performance.
Solution Approach 2:
The hearing device performs self-learning and self-adjustment without requiring external intervention or repeated dispenser visits. The device autonomously collects operating data, processes user feedback, and updates its own parameters through continuous learning, thereby achieving high adaptability while maintaining relatively simple device architecture.
2Reliability
If hearing device parameters are continuously updated in real-time, then user satisfaction is improved, but device complexity and computational requirements worsen
Solution Approach 1:
The system updates parameters selectively based on detected needs rather than continuously adjusting all parameters at full computational capacity. The learning model processes data and updates only when meaningful improvements can be made, balancing user satisfaction with computational efficiency and device complexity constraints.
Solution Approach 2:
The patent introduces an intermediary learning model that acts as a computational bridge between raw operating data and final parameter adjustments. This intermediary layer processes and filters information, enabling reliable real-time adaptation while managing computational complexity through efficient data processing and selective parameter updates.
3Adaptability or versatility
If hearing device parameters are updated based on user feedback, then adaptability is improved, but time consumption for data collection and processing worsens
Solution Approach 1:
The system performs continuous learning and parameter optimization during normal device operation rather than requiring separate calibration sessions. The learning process runs continuously in the background, collecting and processing data without interrupting the user's listening experience, thereby achieving personalization without significant time loss.
Solution Approach 2:
The learning model is pre-initialized with baseline parameters and continues to refine them over time. By having preliminary parameter settings ready and continuously improving them, the system achieves rapid adaptation without requiring extensive data collection periods, reducing the time needed for effective personalization.
Data Source
AI summary
Hearing system, accessory device, agent and method for situated design of a hearing algorithm of a hearing device is disclosed, the method comprising initializing a model comprising a parameterized objective function; providing one or more operating parameters indicative of the hearing algorithm to the hearing device; obtaining operating data comprising corresponding input data and output data of the hearing device; obtaining evaluation data indicative of user evaluation of the output data; determining one or more updated operating parameters based on the model, the operating data and the evaluation data; and providing the updated operating parameters to the hearing device.