Vehicle Active Noise Reduction With Fast-Converging FxLMS Control
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Solution Overview
Problem
Existing active noise reduction methods for vehicles, such as the LMS and momentum-based FxLMS algorithms, suffer from slow convergence speeds, particularly when addressing low-frequency noise from vehicle engines, and are inefficient in reducing noise pollution and improving listening comfort.
Innovation Solution
An improved momentum-based FxLMS algorithm that utilizes vehicle-mounted audio systems to generate a secondary sound wave for noise cancellation, employing adaptive control parameters and error signals to enhance convergence speed, including steps for generating reference signals, filtering, and updating control parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional LMS algorithm or momentum-based FxLMS algorithm is used for active noise reduction, then the system can reduce engine noise, but the convergence speed is slow
Solution Approach 1:
The patent changes the algorithmic parameters by introducing a momentum term and modifying the weight update rule in the FxLMS algorithm. This transforms the original slow-converging LMS algorithm into a faster-converging momentum-based FxLMS algorithm while maintaining noise reduction effectiveness
Solution Approach 2:
The patent implements feedback mechanisms where the estimated secondary path transfer function is continuously updated and used to adjust the control filter weights. This feedback loop enables the system to adapt to changing acoustic conditions and achieve faster convergence
2Reliability
If passive noise control methods are used, then medium and high-frequency noise is reduced effectively, but low-frequency engine noise reduction is poor and adjusting time is long
Solution Approach 1:
The patent replaces passive mechanical noise control methods (damping materials, shock-absorbing springs) with an active electronic control system. The electronic algorithm can rapidly adapt to low-frequency engine noise without requiring physical structural modifications or long adjustment periods
Solution Approach 2:
The patent implements a dynamic adaptive filtering system where the control filter weights are continuously updated in real-time based on the incoming noise signal. This dynamic adaptation allows the system to respond quickly to changing low-frequency engine noise conditions, unlike static passive control methods
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The algorithm achieves faster convergence, reducing noise pollution and improving listening comfort by effectively canceling out vehicle engine noise within 1800 iterations, while maintaining a lightweight and energy-efficient solution.
Implementation Method 1
the reference signal can be converted into a control signal to drive a sound reproduction device, so as to generate a secondary sound wave
Implementation Method 2
an acoustic signal and its frequency are obtained from an acoustic signal acquisition device
Data Source
AI summary
Disclosed are an active noise reduction method and device for a vehicle, and a storage medium. The active noise reduction method includes the following steps: generating two reference signals according to a target noise to be denoised; generating a control signal and sending it to a sound reproduction device; filtering the reference signals to obtain filtered reference signals; calculating a noise signal; updating an auxiliary control parameter; calculating an updated auxiliary control parameter-based error signal; and updating the control parameter. The method actively reduces the noise of vehicle engines, reduces interior noise pollution, and has a fast convergence speed.