Hearing Aid Listening Situation Classifier Training
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Solution Overview
Problem
Conventional hearing aids often fail to accurately match acoustic situations with predefined listening situations, leading to unsatisfactory signal processing for users, as their classifiers rely solely on pre-stored training data and may incorrectly identify or fail to recognize certain environments.
Innovation Solution
A method and system that involve user interaction to adapt training data for the listening situation classifier by presenting acoustic signals and prompting users to indicate the signal source, allowing for continuous improvement and updating of the classifier, especially for unknown acoustic situations, through a combination of manual input, gamification, and data storage in a central database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier is trained using only pre-stored training data from a database, then the device complexity is reduced and ease of manufacture is improved, but the measurement precision and reliability of acoustic situation identification deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the hearing aid wearer's indications of signal sources are fed back to adapt and update the training data for the listening situation classifier. This closed-loop feedback system allows the classifier to learn from actual user experiences and continuously improve its identification accuracy without requiring complete retraining from scratch.
Solution Approach 2:
The system enables self-service learning where the hearing aid automatically collects new acoustic situations, presents them to the user for identification, and uses the user's input to automatically update its own training data and improve its classification performance over time without external intervention.
2Adaptability or versatility
If the classifier is updated continuously with new training data from user input, then the adaptability and measurement precision improve, but the loss of time for training and processing increases
Solution Approach 1:
The patent applies partial action by updating the training data selectively rather than completely retraining the classifier each time new data is collected. Only the specific acoustic situations and signal source identifications that are newly learned are added to the training database, allowing incremental adaptation without the time cost of full retraining.
3Measurement precision
If the user is involved in indicating signal sources for training, then the measurement precision and adaptability improve, but the ease of operation decreases due to additional user interaction requirements
Solution Approach 1:
The system is designed to be minimally intrusive by automatically managing the entire training process except for the user's simple signal source indication. The hearing aid autonomously collects acoustic data, presents it to the user, processes the user's input, and automatically updates its training database, requiring only simple user participation while maintaining high precision.
Data Source
AI summary
In a method for the training of a listening situation classifier for a hearing aid, a user is presented with a number of acoustic signals by use of a terminal device, which prompts the user to indicate the signal source of the signal or the particular presented signal. The training data is adapted for the listening situation classifier in dependence on the user's indication of the presented signal or one of the possibly several signals presented and updates the listening situation classifier by use of the training data.