Hearing Device Neural Network Customization
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Solution Overview
Problem
Existing hearing device systems face challenges in processing audio signals with low latency and efficiency, often resulting in perturbing delays and echo effects, while also requiring external devices for signal processing which increases latency.
Innovation Solution
A hearing device system with a customizable and replaceable neural network, where the calibration device handles neural network customization and replacement independently of the hearing device, allowing for real-time signal processing with minimal latency and efficient power consumption, and enabling selective processing of audio signals from various sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external devices are used for signal processing, then processing quality can be improved, but latency increases and echo effects occur
Solution Approach 1:
The patent extracts the neural network processing capability from external calibration devices and embeds it directly into the hearing device. This allows the hearing device to independently perform high-quality signal processing without transmitting data externally, thereby eliminating transmission latency and echo effects while maintaining processing quality.
Solution Approach 2:
The patent introduces a lightweight adapter layer that mediates between the hearing device hardware and the neural network. This adapter enables the hearing device to execute complex neural network operations locally without requiring continuous external intervention, thus reducing latency while preserving processing quality.
2Adaptability or versatility
If neural networks are made customizable and replaceable, then adaptability to different hearing situations improves, but device complexity increases
Solution Approach 1:
The patent segments the neural network into modular components that can be independently selected and replaced. The calibration device provides a library of specialized neural networks for different hearing situations, and the hearing device can switch between them as needed. This modular approach enables high adaptability without proportionally increasing the core device complexity.
Solution Approach 2:
The patent implements dynamic neural network selection where the system can switch between different neural network configurations based on real-time hearing conditions. The calibration device dynamically updates and replaces neural networks in the hearing device, allowing the system to adapt to changing requirements without requiring permanent complex hardware.
3Speed
If real-time processing is implemented with minimal latency, then user experience improves, but power consumption increases
Solution Approach 1:
The patent implements partial neural network execution where only the essential processing operations are performed in real-time on the hearing device, while less critical operations are deferred or performed with lower computational intensity. This approach achieves acceptable real-time performance for critical audio processing without proportionally increasing power consumption.
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with optimized neural network architectures that require less computational power for real-time operation. By using specialized lightweight neural network models and efficient inference techniques, the system achieves real-time processing speeds with reduced energy consumption compared to conventional approaches.
Data Source
AI summary
A hearing device system and the method for processing audio signals are described. The hearing device system has at least one hearing device having a recording device for recording an input signal, at least one neural network for separating at least one audio signal from the input signal and a playback device for playing back an output signal ascertained from the at least audio signal. A calibration device is connected to the at least one hearing device in the data-transmitting manner. The at least one neural network is customizable and/or replaceable by the calibration device.