On-Demand Adaptive ANC Filter Update Mechanism
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Solution Overview
Problem
Existing adaptive noise cancellation systems in personal audio devices face challenges in maintaining optimal noise attenuation due to varying ambient conditions and headset fits, leading to potential misadaptation and increased battery drain from continuous adaptation.
Innovation Solution
An integrated circuit with a user-triggered adaptive filter update mechanism that assesses ambient conditions and prevents filter updates during undesirable conditions, using a preselected training signal to optimize ANC filter coefficients while conserving battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous adaptation is implemented to maintain optimal noise cancellation, then noise attenuation performance is improved, but battery drain increases
Solution Approach 1:
The system implements periodic adaptation at user-triggered events (such as when the device is inserted into the ear) rather than continuous adaptation. This allows the ANC filters to be updated periodically to maintain optimal performance while significantly reducing battery consumption compared to continuous adaptation.
Solution Approach 2:
The system performs adaptation in advance at specific trigger events (device insertion, user initiation) before the user experiences degraded performance. By adapting preliminarily at these key moments, the system ensures optimal noise cancellation is ready when needed without requiring continuous power-intensive adaptation.
2Stability of the object's composition
If forced adaptation with preprogrammed training signal is used to ensure stable adaptation, then adaptation stability is improved, but adaptability to varying ambient conditions deteriorates
Solution Approach 1:
The system uses dynamic adaptation where the training signal is not fixed but adapts to the current ambient conditions. The adaptation process remains stable through controlled algorithms while the training content dynamically changes based on the acoustic environment, allowing the system to maintain both stability and adaptability.
Solution Approach 2:
The system automatically selects and adjusts the training signal based on the detected ambient conditions without requiring manual user configuration. This self-adjusting mechanism ensures stable adaptation processes while maintaining high adaptability to different acoustic environments.
3Reliability
If fully adaptive ANC system is implemented to compensate for environmental changes, then noise cancellation effectiveness is improved, but system complexity increases
Solution Approach 1:
The system implements partial adaptation by updating ANC filters only at specific trigger events rather than continuously or in response to every environmental change. This partial action approach maintains effective noise cancellation for the most significant environmental changes while avoiding the complexity of continuously monitoring and adapting to all possible variations.
Data Source
AI summary
A method may include receiving a user trigger signal indicating a user desire to update characteristics of an adaptive filter, receiving an error microphone signal indicative of the output of the transducer and the ambient audio sounds at the transducer, wherein the transducer reproduces both a source audio signal for playback to a listener and an anti-noise signal for countering the effects of ambient audio sounds in an acoustic output of the transducer, implementing the adaptive filter having a response that generates the anti-noise signal to reduce the presence of the ambient audio sounds in the error microphone signal, determining an acoustic coupling of the transducer to an error microphone for producing the error microphone signal, and responsive to a change in the acoustic coupling, prompting a user to assert the user trigger signal to indicate user desire to update characteristics of the adaptive filter.