Subband Virtual Path Calculation for High-Frequency Noise Cancellation
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Solution Overview
Problem
Current virtual microphone technology (VMT) systems in active noise cancellation face limitations in estimating high-frequency noise and require significant computational power, which increases space and power consumption in vehicles.
Innovation Solution
Implementing subband adaptive filtering (SAF) to decompose signals into subbands, calculate subband gradients, and adjust step sizes for enhanced virtual path estimation, reducing computational complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a LMS algorithm is used to calculate the virtual path, then the system can perform noise cancellation, but the computational power required increases significantly
Solution Approach 1:
The patent divides the frequency spectrum into multiple subbands and processes each subband separately using independent adaptive filters. This segmentation reduces the computational complexity of the overall system by breaking down the full-band LMS algorithm into multiple simpler subband processors, thereby reducing power consumption while maintaining noise cancellation effectiveness across different frequency ranges
Solution Approach 2:
The patent replaces the traditional time-domain LMS algorithm with a frequency-domain subband processing approach. By transforming the signal processing from time domain to frequency domain through subband decomposition, the system achieves more efficient computation with reduced operational complexity and lower power requirements
2Reliability
If a LMS algorithm is used to estimate high frequency noise, then noise cancellation can be attempted, but the estimation accuracy is limited
Solution Approach 1:
By dividing the frequency spectrum into multiple subbands, the system can specifically target and process high-frequency components with dedicated subband adaptive filters. This allows for improved estimation accuracy in the high-frequency range where the traditional LMS algorithm struggles, while maintaining overall system performance across all frequency bands
Solution Approach 2:
The patent applies different processing characteristics to different frequency subbands. Each subband can be optimized independently with appropriate filter parameters and step sizes tailored to its specific frequency characteristics, thereby improving the local quality of noise estimation and cancellation particularly in the high-frequency regions
3Productivity
If computational devices are increased to improve processing capability, then the algorithm can perform effectively, but the space required in the vehicle increases
Solution Approach 1:
The subband processing architecture distributes computational tasks across multiple independent, smaller-scale processors rather than requiring one large complex processor. This segmentation allows for more efficient use of computational resources and reduces the overall hardware volume required to achieve the same processing effectiveness
Solution Approach 2:
By transitioning to frequency-domain subband processing, the patent reduces the computational burden on hardware, allowing effective noise cancellation with less powerful (and therefore smaller) computational devices, thus reducing the space required in the vehicle
Data Source
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AI summary
Methods and systems are disclosed for a vehicle audio system. In one example, a method for noise cancellation in a vehicle having a physical microphone configured to acquire a physical microphone signal, and a plurality of virtual microphones acquiring a residual signal is provided, including processing the physical microphone signal with an adaptive weight filter to estimate a virtual secondary path from the physical microphone to the plurality of virtual microphones, decomposing the residual signal and the physical microphone signal into a plurality of subband signals, determining a subband gradient for each subband, determining a subband virtual path convergence speed based on a normalized step size for each subband, determining a subband virtual path for each subband based on the normalized step size and the subband gradient, and applying a weight transformation process to each subband virtual path to update the adaptive weight filter and verify the subband virtual path.