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 improved virtual path estimation, reducing computational complexity and enhancing 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 parallel subband processors, each handling a specific frequency range with reduced computational demands.
Solution Approach 2:
The patent transforms the time-domain LMS algorithm into the frequency domain by applying a Fourier transform to the adaptive filter. This dimensional change from time domain to frequency domain enables more efficient computation of the virtual path, reducing the computational power required while maintaining noise cancellation effectiveness.
2Reliability
If a LMS algorithm is used to calculate the virtual path, then the system can estimate noise, but the accuracy for high frequency noise estimation is limited
Solution Approach 1:
By dividing the frequency spectrum into multiple subbands, the patent enables each subband processor to focus on a specific frequency range. This segmentation allows for more accurate high-frequency noise estimation in the high-frequency subbands, as each processor can be optimized for its specific frequency range rather than trying to handle all frequencies uniformly.
Solution Approach 2:
The patent applies different processing characteristics to different subbands, with each subband having its own adaptive filter optimized for its specific frequency range. This local quality approach enables high-frequency subbands to use parameters and algorithms specifically tuned for high-frequency noise characteristics, thereby improving measurement precision for high-frequency noise estimation.
3Measurement precision
If computational devices are increased to improve virtual path calculation accuracy, then noise cancellation performance improves, but the space required in the vehicle increases
Solution Approach 1:
The patent uses subband decomposition to divide the computational task into multiple smaller, parallel subband processors. This segmentation reduces the computational burden on each individual processor, allowing the system to achieve high virtual path calculation accuracy using smaller, more compact computational devices that fit within vehicle space constraints.
Solution Approach 2:
By transforming the computational approach from time domain to frequency domain, the patent reduces the computational complexity and processing power required. This enables the use of smaller, more space-efficient computational devices while maintaining or improving virtual path calculation accuracy through frequency-domain processing efficiency.
Data Source
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.