Hearing Device Visual Scene Analysis for Adaptive Sound Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Hearing devices struggle to optimize hearing performance in varying environments due to users' unawareness or inability to apply effective hearing strategies, despite the availability of such strategies, leading to suboptimal sound processing.
Innovation Solution
A system and method that utilizes image analysis and machine learning to determine current and expected hearing performance levels, providing real-time feedback and guidance for improving positioning and sound processing strategies based on environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hearing devices process sound signals with advanced algorithms, then hearing performance is improved, but device complexity increases
Solution Approach 1:
The patent introduces a camera as an intermediary device that captures images of the acoustic environment. These images are processed by a machine learning model to generate environment labels, which then guide the hearing device's signal processing. This intermediary approach allows complex environmental analysis without directly increasing the hearing device's computational complexity.
Solution Approach 2:
The patent replaces traditional acoustic-based environment detection (microphone arrays, signal processing) with an optical-based system (camera imaging). The machine learning model processes visual information to infer acoustic environment characteristics, substituting mechanical/acoustic systems with optical and computational approaches.
2Adaptability or versatility
If hearing devices adapt to different environments, then hearing performance is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically capturing images, analyzing the acoustic environment through machine learning, and adjusting hearing parameters without user intervention. The hearing device autonomously adapts to different environments based on visual scene analysis, eliminating the need for manual user configuration.
Solution Approach 2:
The patent implements a feedback loop where the camera continuously monitors the environment, the machine learning model analyzes the images to determine environment type, and the hearing device adjusts its parameters accordingly. This closed-loop feedback system enables automatic adaptation to changing acoustic environments.
3Reliability
If hearing devices provide real-time environment analysis, then hearing performance is improved, but use of energy increases
Solution Approach 1:
Instead of continuous real-time processing, the system uses periodic action by capturing images at specific intervals or when environment changes are detected. The machine learning model processes these discrete images to update hearing parameters, reducing continuous computational energy consumption while maintaining effective environment adaptation.
Data Source
AI summary
An exemplary method includes a hearing system accessing one or more images of an environment of a user located at a position within the environment and wearing a hearing device to hear sound within the environment, determining a current hearing performance level being provided by the hearing device worn by user at the position, determining, based on the one or more images, one or more expected hearing performance levels at one or more other positions in the environment, and performing, based on the determining of the current hearing performance level and the one or more expected hearing performance levels, a hearing performance enhancement operation configured to improve the current hearing performance level.