Adaptive Acoustic Feedback Mitigation for Changing Sound Paths
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Solution Overview
Problem
Existing systems face challenges in effectively mitigating acoustic feedback (AFB) between acoustic transducers and sensors, particularly in dynamic environments where the acoustic medium is not fixed or constant, such as due to changes in temperature, humidity, or physical location.
Innovation Solution
An adaptive Acoustic Feedback (AFB) mitigator is implemented using an adaptive filter, such as a Finite Impulse Response (FIR) or Infinite Impulse Response (IIR) filter, combined with a Least Mean Squares (LMS) algorithm, to adapt to varying acoustic conditions and mitigate AFB in AAC systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional AFB mitigation methods are used, then system complexity is reduced, but adaptability to changing acoustic conditions deteriorates
Solution Approach 1:
The patent implements dynamic adaptation by using adaptive filters (FIR or IIR) with coefficients that are continuously updated in real-time based on the acoustic environment. The system transitions from static to dynamic operation, allowing the AFB mitigation to adapt to changing conditions such as varying acoustic paths, temperature, and humidity without requiring manual reconfiguration.
Solution Approach 2:
The system employs feedback mechanisms where the output of the acoustic sensor is fed back to the adaptive filter, which continuously adjusts its coefficients based on the correlation between transducer output and sensor input. This closed-loop feedback enables the system to automatically track and compensate for changes in the acoustic medium, resolving the contradiction between adaptability and complexity.
2Reliability
If adaptive filters with real-time adaptation are implemented, then AFB mitigation performance is improved, but computational requirements increase
Solution Approach 1:
The patent changes the parameters of the filter coefficients in real-time based on the acoustic conditions. By dynamically adjusting these parameters (filter coefficients) rather than maintaining fixed values, the system achieves superior AFB mitigation performance. The LMS algorithm efficiently computes these parameter changes, balancing performance improvement with computational feasibility.
Solution Approach 2:
The system applies adaptive filtering selectively to the frequency ranges and time periods where AFB is most problematic, rather than processing all signals at maximum computational intensity. The adaptive filter converges to optimal coefficients over time, providing sufficient mitigation performance without requiring excessive continuous computational resources.
3Reliability
If the system adapts to highly correlated transducer and sensor outputs, then noise cancellation effectiveness is maintained, but system stability may be compromised
Solution Approach 1:
The adaptive filter acts as an intermediary between the transducer output and the acoustic sensor input. It processes the highly correlated signals through a controlled computational framework, preventing direct feedback instability while maintaining noise cancellation effectiveness. The filter coefficients serve as a mediator that transforms the correlated inputs into appropriate cancellation signals without causing system oscillation or divergence.
Data Source
AI summary
For example, an Acoustic Feedback (AFB) mitigator may mitigate AFB between at least one acoustic transducer and at least one acoustic sensor. For example, the AFB mitigator may include a first filter to generate a first filtered signal by filtering a first input signal, the first input signal nay be based on a transducer acoustic pattern to be output by the acoustic transducer; and a second filter to generate a second filtered signal by filtering the first input signal, wherein the second filter may include an adaptive filter, which may be adapted based on a difference between an AFB-mitigated signal and the second filtered signal. For example, the AFB-mitigated signal may be based on a difference between a second input signal and the first filtered signal, the second input signal based on a sensor acoustic pattern sensed by the acoustic sensor.