Hearing System Audio Processing with Neural Confidence Steering
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Solution Overview
Problem
Neural networks used in audio signal processing on hearing devices can fail for complex audio signals, leading to artefacts and reduced quality of processed audio.
Innovation Solution
A method that includes determining a confidence parameter to assess the reliability of neural network processing and using this parameter to steer audio signal processing, ensuring more robust and reliable results, even in complex scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If neural network processing is used for audio signal processing on hearing devices, then processing quality and performance are improved, but reliability deteriorates for complex audio signals
Solution Approach 1:
The system implements a feedback mechanism by determining a confidence parameter that reflects the neural network's certainty about its processing output. This confidence parameter is then fed back into the system to dynamically adjust processing strategies, allowing the system to respond to uncertain predictions and switch to alternative processing methods when reliability is low.
Solution Approach 2:
The processing system transitions from a static neural network approach to a dynamic hybrid approach. The system adapts its processing strategy in real-time based on the confidence parameter, switching between neural network processing and conventional audio signal processing methods depending on the reliability assessment for different audio scenarios.
2Reliability
If conventional audio signal processing is used, then reliability is maintained, but processing quality deteriorates for complex audio signals
Solution Approach 1:
Instead of using conventional processing for all scenarios, the system applies it partially - only when the neural network's confidence parameter indicates low reliability. This selective application allows the system to benefit from neural network superiorities in suitable conditions while falling back to conventional methods when needed.
Solution Approach 2:
The system creates a composite processing approach by combining neural network processing and conventional audio signal processing methods. This hybrid system leverages the strengths of both approaches, using neural networks for high-confidence scenarios and conventional methods for low-confidence scenarios, achieving overall improved reliability and quality.
3Manufacturing precision
If neural network processing is applied to all audio signals, then processing quality is improved, but artefacts increase for complex audio signals
Solution Approach 1:
The system takes preliminary action by assessing the confidence parameter before finalizing neural network processing output. When low confidence is detected, the system preemptively switches to conventional processing or adjusts the neural network output, preventing artefacts from occurring in the first place rather than correcting them afterward.
Data Source
AI summary
Methods, hearing systems and neural networks for audio signal processing are described. A hearing system comprises a neural network. An input audio signal is processed into an output audio signal at least partially by means of a processing unit of the hearing system. The audio signal processing of the input audio signal includes executing audio signal processing by the neural network for performing a step of the audio signal processing, determining a confidence parameter resembling the reliability of the audio signal processing by the neural network and steering the audio signal processing in dependence of the confidence parameter. A particularly suitable neural network is configured to solve a regression-based acoustic processing task and additionally determining the confidence parameter.