Neural Network Sound Processing for Personalized Hearing Prostheses
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Solution Overview
Problem
Conventional hearing aids and cochlear implants struggle to provide personalized and effective sound processing tailored to individual recipients, lacking the ability to adapt to specific needs and characteristics.
Innovation Solution
A hearing prosthesis system utilizing a neural network interposed between an input subsystem and an output subsystem, which processes sound data through machine learning to stimulate tissue and evoke a sensory percept, including a deep neural network (DNN) for advanced signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional hearing aids or cochlear implants are used, then basic sound transmission or electrical stimulation is provided, but personalized and adaptive sound processing tailored to individual recipients is lacking
Solution Approach 1:
A neural network is introduced as an intermediary component between the sound input subsystem and the electrical stimulation output subsystem. This neural network processes acoustic signals and generates optimized electrical stimulation patterns tailored to individual recipient characteristics, enabling personalized sound processing without requiring complex adjustments to the entire device architecture.
Solution Approach 2:
The system utilizes machine learning algorithms to dynamically adjust stimulation parameters (such as electrode selection, stimulation intensity, and pulse patterns) based on individual recipient feedback and performance data. This allows the device to adapt to specific recipient needs by changing operational parameters rather than requiring structural modifications.
2Adaptability or versatility
If machine learning and neural networks are introduced for personalized processing, then adaptability improves, but device complexity increases
Solution Approach 1:
The processing system is divided into distinct functional modules: an input subsystem for capturing acoustic signals, a neural network processing layer for intelligent signal analysis and stimulation pattern generation, and an output subsystem for electrical stimulation delivery. This segmentation allows each component to be optimized independently and facilitates easier implementation and adjustment of the machine learning algorithms.
3Measurement precision
If individualized fitting and customization are performed, then hearing perception quality improves, but the fitting process becomes more time-consuming and complex
Solution Approach 1:
The neural network system incorporates automated feedback mechanisms that allow it to learn and adapt to individual recipient characteristics during the fitting process. The system can automatically adjust stimulation parameters based on real-time performance data and recipient responses, reducing the need for extensive manual fitting procedures and expert intervention while maintaining high precision in hearing perception.
Data Source
AI summary
A method, wherein the method includes obtaining data, wherein, the data contains audio content, visual content, or audio content and visual content processing data based on the audio and/or visual content using results from machine learning to develop output, and stimulating tissue of a recipient to evoke a sensory percept based on the output.