Dual-Filter Noise Cancellation Using Adaptive Coefficient Updates
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Solution Overview
Problem
The existing single-filter structure LMS algorithm for noise elimination uses FIR filters, which introduce errors and result in a poor noise elimination effect due to their inability to accurately approach the optimal non-FIR filter solution.
Innovation Solution
A method and device that adaptively update the coefficients of two filters in noise segments to match the ratio of channel transfer functions between noise and microphone sources, using either least mean square or fast block least mean square algorithms, and maintain these coefficients in noisy voice segments to enhance noise cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single FIR filter structure is used for noise elimination, then the filter is stable and easy to implement, but the noise elimination effect is poor due to inability to approach the optimal non-FIR filter solution
Solution Approach 1:
The patent divides the single filter into two separate filters (first filter and second filter). Each filter processes signals from different microphones independently, allowing them to better approach the optimal non-FIR filter solution while maintaining stability. The segmentation enables each filter to be optimized for its specific channel without compromising overall system stability.
Solution Approach 2:
The patent extracts the noise component by separating the signal processing into two independent filter paths. By taking out the noise elimination function into a dual-filter structure where each filter handles specific microphone inputs, the system can achieve better noise cancellation while maintaining the stability of individual FIR filter implementations.
2Adaptability or versatility
If the filter coefficient is updated continuously in all segments, then the noise elimination adapts to changing conditions, but the computational complexity increases and may cause instability in noisy voice segments
Solution Approach 1:
The patent implements periodic updating of filter coefficients by alternating between noise segments and noisy voice segments. During noise segments, coefficients are updated to adapt to changing noise conditions. During noisy voice segments, coefficients remain unchanged to avoid computational complexity and potential instability. This periodic action balances adaptability with computational efficiency.
Solution Approach 2:
The patent makes the filter coefficient updating dynamic by adjusting the update frequency based on the signal segment type. The system dynamically switches between updating coefficients (in noise segments) and holding coefficients constant (in noisy voice segments), optimizing the balance between adaptability and computational complexity for different operating conditions.
3Measurement precision
If standard time domain LMS algorithm is used to compute convolutional non-additive interference noises, then the noise elimination is accurate, but the computation is relatively complicated
Solution Approach 1:
The patent substitutes the time domain convolution operation with frequency domain multiplication using Fast Block LMS algorithm. By replacing the computationally intensive time domain convolution with simpler frequency domain operations, the system maintains noise elimination accuracy while significantly reducing computational complexity.
Solution Approach 2:
The patent changes the domain parameter from time domain to frequency domain for filter coefficient computation. This parameter change enables the use of Fast Block LMS algorithm which reduces computational complexity while maintaining the accuracy of noise elimination through frequency domain product operations instead of time domain convolution.
Data Source
AI summary
The present invention discloses a method and device for self-adaptively eliminating noises. Said method comprises: filtering the signal received by a first microphone using a first filter, filtering the signal received by a second microphone using a second filter, and obtaining a signal with noises reduced by subtracting the filtered signals; wherein, in a noise segment, the coefficients of the first filter the second filter are updated respectively using the signal with noises reduced such that the noise component contained in the signal filtered by the first filter tends to be the same with the noise component contained in the signal filtered by the second filter; and in a noisy voice segment, the coefficients of the first filter and the second filter are remained unchanged respectively, the first filter and the second filter respectively use a coefficient updated in the noise segment last time to filter the signals received by the first microphone and the second microphone. The present invention can address the problem that noise eliminating effect is poor in the prior art caused by the fact that FIR filter cannot approach the optimal solution for eliminating noises.