Feedback Suppressor Circuit Using Parameter Repository for Audio Systems
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Solution Overview
Problem
Existing audio systems, such as hearing aids, face challenges in achieving a balance between low steady-state error and fast tracking in feedback cancellation, as adaptive filters often perform sub-optimally under steady-state conditions and are slow to adapt in dynamic situations.
Innovation Solution
An audio system with a feedback suppressor circuit that stores sets of feedback model parameters for different signal paths, allowing for rapid retrieval and adaptation when recurring paths are detected, using a combination of adaptive filters and clustering techniques to maintain low steady-state error and fast transient response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adaptive filters are used for feedback cancellation, then the system can adapt to changing feedback paths, but the tracking speed is slow in dynamic situations
Solution Approach 1:
The system performs preliminary action by storing multiple pre-computed feedback model parameter sets corresponding to different feedback signal paths in a repository. When a path change occurs, the system retrieves the pre-stored parameters immediately rather than computing them in real-time, thus achieving fast tracking while maintaining adaptability.
Solution Approach 2:
The feedback parameter space is segmented into multiple discrete parameter sets, each corresponding to a specific feedback signal path condition. This segmentation allows the system to switch between pre-defined parameter sets based on the current operating condition, enabling rapid adaptation without continuous computation.
2Speed
If adaptive filters continuously adapt to track changes, then the system responds quickly to dynamic situations, but the steady-state error increases
Solution Approach 1:
The system performs preliminary computation offline to generate accurate feedback model parameter sets for various feedback path conditions. These pre-computed parameters achieve low steady-state error, and during operation, the system simply retrieves the appropriate pre-computed set rather than continuously adapting, thus maintaining precision while enabling fast response.
Solution Approach 2:
The system dynamically switches between different pre-computed parameter sets based on the detected feedback path condition. This dynamic selection approach allows the system to maintain optimal steady-state performance for the current condition while achieving fast transient response when conditions change.
3Adaptability or versatility
If the adaptive filter re-calculates coefficients after path changes, then the system adapts to new feedback paths, but the time required for re-adaptation increases
Solution Approach 1:
The system performs the computationally intensive filter coefficient calculation in advance for multiple possible feedback path conditions and stores these pre-computed parameter sets. When a feedback path change is detected, the system retrieves the corresponding pre-computed parameters immediately, eliminating re-adaptation time while maintaining full adaptability to new paths.
Solution Approach 2:
The system creates copies of feedback model parameters for different feedback path conditions and stores them in a repository. Instead of recalculating parameters when conditions change, the system copies the appropriate pre-computed parameter set from storage, achieving instantaneous adaptation without re-computation.
Data Source
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AI summary
The present invention relates to an audio system comprising a signal processor for processing an audio signal, and a feedback suppressor circuit configured for modelling a feedback signal path of the audio system by provision of a feedback compensation signal based on sets of feedback model parameters for the feedback signal path that are stored in a repository for storage of the sets of feedback model parameters.