Automatic Tuning of Perceptual Devices via User Feedback
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Solution Overview
Problem
Complex perceptual devices, such as digital hearing aids, are difficult to tune to individual user needs due to a large number of parameters and the time-consuming process of testing, often resulting in suboptimal performance for users, especially those with complex perceptual systems like humans.
Innovation Solution
A method and system that automatically tune perceptual devices by generating input signals, receiving output signals, constructing a perceptual model, and suggesting parameter values based on user feedback, utilizing algorithms and knowledge bases to reduce the complexity of parameter adjustment and expedite the tuning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tuning methods are used for complex perceptual devices, then customization to user needs is possible, but the time and cost required becomes too high
Solution Approach 1:
The system enables automatic self-tuning of perceptual devices by having the device test and adjust its own parameters based on user responses, eliminating the need for manual intervention by audiologists or technicians. The device autonomously generates test signals, collects user feedback, and modifies parameters to optimize performance.
Solution Approach 2:
The system automatically modifies device parameters based on user responses to test signals. By dynamically changing parameters according to measured user performance, the system achieves customization without manual intervention, resolving the contradiction between adaptability and time consumption.
2Manufacturing precision
If comprehensive user testing is conducted to determine optimum parameter values, then user-specific performance is optimized, but the number of tests required increases exponentially with device parameters
Solution Approach 1:
The system divides the parameter optimization process into manageable segments by testing parameters independently or in small groups rather than requiring exhaustive testing of all parameter combinations. This segmentation reduces the exponential complexity while maintaining optimization accuracy through iterative refinement.
Solution Approach 2:
The system performs partial testing on a subset of parameters or uses simplified test procedures that capture the essential user needs without conducting exhaustive comprehensive tests. This approach achieves sufficient optimization without the exponential time cost of complete parameter space exploration.
3Ease of manufacture
If factory default settings are used for cochlear implants, then device deployment is simplified, but users cannot achieve full benefit of the implant
Solution Approach 1:
The system performs preliminary automatic tuning immediately after device implantation or activation, establishing optimized parameters before the user begins normal use. This preliminary action ensures both ease of deployment and optimal performance from the outset.
Solution Approach 2:
The system continuously monitors user responses and uses feedback to automatically adjust parameters, ensuring that users achieve full benefit from their implants while maintaining simple deployment procedures. The feedback loop bridges the gap between default settings and customized optimization.
Data Source
AI summary
Systems and methods may be used to modify a controllable stimulus generated by a digital audio device in communication with a human user. An input signal is provided to the digital audio device. In turn, the digital audio device sends a stimulus based on that input signal to the human user, who takes an action, usually in the form of an output signal, to characterize the stimulus that the user receives, based on the user's perception. An algorithm, lookup table, or other procedure then determines a difference between the input signal and the output signal, and a perceptual model is constructed based at least in part on the difference. Thereafter, a new value for the parameter of the digital audio device is suggested based at least in part on the perceptual model. This process continues iteratively until the user's optimal device parameters are determined.