Adaptive Noise-Canceling Filter Coefficient Selection
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Solution Overview
Problem
Existing adaptive noise-canceling systems for personal audio devices are complex, power-intensive, and prone to instability due to changes in the acoustic environment, including variations in device positioning and earpiece fit.
Innovation Solution
An adaptive noise-canceling system that uses a filter with dynamically selectable coefficients based on phase differences between noise measurement signals from multiple microphones, allowing for efficient adaptation to changing acoustic environments while minimizing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adaptive noise canceling circuits continuously adapt to ambient noise and device position changes, then noise canceling effectiveness is improved, but system complexity and power consumption increase
Solution Approach 1:
The system dynamically switches between different filter coefficient sets based on detected acoustic environment conditions, allowing the noise canceling characteristics to adapt to changing conditions without continuously recalculating all parameters. This provides adaptability while limiting complexity through discrete pre-defined filter sets rather than continuous adaptation mechanisms
Solution Approach 2:
The system changes key parameters (filter coefficients) based on detected phase differences between microphone signals, allowing the noise canceling performance to be adjusted for different acoustic environments. By changing only the coefficient parameters rather than the entire system architecture, the patent achieves adaptability with controlled complexity
2Reliability
If adaptive noise canceling circuits continuously adapt to ambient noise and device position changes, then noise canceling effectiveness is improved, but power consumption increases
Solution Approach 1:
The system uses dynamic switching between pre-computed filter coefficient sets based on real-time phase difference measurements, rather than continuously computing new coefficients. This approach maintains noise canceling effectiveness across varying conditions while significantly reducing the computational power required compared to continuous adaptation algorithms
Solution Approach 2:
Multiple filter coefficient sets are pre-computed and stored before operation, representing different acoustic environment conditions. During operation, the system only needs to select the appropriate pre-computed set based on measured phase differences, avoiding the need for real-time complex calculations and reducing power consumption
3Adaptability or versatility
If adaptive noise canceling circuits are designed to handle all acoustic environments, then adaptability is improved, but system stability deteriorates due to instabilities from environmental changes
Solution Approach 1:
The system adapts its noise canceling characteristics by switching between different filter coefficient sets based on detected phase differences, allowing it to respond to acoustic environment changes. The switching is controlled based on measured conditions, providing adaptability while maintaining stability through deliberate state transitions rather than continuous uncontrolled adaptation
Solution Approach 2:
The system measures phase differences between microphone signals and uses this feedback to select appropriate filter coefficient sets. This closed-loop approach ensures the system adapts to environmental changes while maintaining stability by basing coefficient selection on actual measured conditions rather than continuous uncontrolled adjustment
4Use of energy by moving object
If filter coefficients are dynamically selected based on phase differences, then power consumption is reduced, but measurement precision requirements increase
Solution Approach 1:
The system replaces continuous complex computational adaptation with discrete phase difference measurement and lookup-based coefficient selection. By substituting mathematical continuous optimization with measured phase comparison against pre-defined sets, the patent reduces computational power requirements while placing precise measurement requirements on the phase detection capability
Data Source
AI summary
An adaptive noise-canceling system generates an anti-noise signal with a filter that has a response controlled by a set of coefficients selected from a collection of coefficient sets. The adaptive noise-canceling system includes an acoustic output transducer for reproducing a signal containing the anti-noise signal, a first microphone for measuring ambient noise at a first location to produce a first noise measurement signal, a second microphone for measuring the ambient noise at a second location to generate a second noise measurement signal, and an analysis subsystem for analyzing the first noise measurement signal and the second noise measurement signal. The adaptive noise-canceling system also includes a controller that selects the set of coefficients from the collection of coefficient sets according to a phase difference between the first noise measurement signal and the second noise measurement signal as determined by the analysis subsystem.