Partitioned Frequency-Domain Adaptive Filtering With Error-Corrected Updates
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Solution Overview
Problem
Partitioned block frequency domain adaptive filter devices face high complexity, which can lead to reduced convergence rates and increased algorithmic complexity, especially in applications like acoustic echo cancellation where simplifying the constraining operation is desirable without compromising performance.
Innovation Solution
A partitioned block frequency domain adaptive filter device with a frequency domain adaptive filter and parallel filter update blocks that use an approximated constraining matrix of lower complexity to calculate constrained gradient updates, allowing for reduced complexity without significantly impacting convergence rates, and includes a correction sequence to address cumulative errors dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full constraining matrix is applied to calculate constrained gradient updates, then convergence rate is improved, but device complexity increases
Solution Approach 1:
The filter coefficients are divided into multiple blocks, and the constraining matrix operation is segmented to process only relevant blocks. This reduces the overall computational complexity while maintaining convergence performance by focusing computational resources on critical filter coefficient updates.
Solution Approach 2:
Instead of applying the full constraining matrix to all filter coefficient blocks, the invention applies the constraining operation selectively to only those blocks that require updating. This partial action approach reduces computational complexity while maintaining sufficient convergence rate for practical applications.
2Device complexity
If the constraining operation is simplified to reduce complexity, then device complexity is reduced, but convergence rate deteriorates
Solution Approach 1:
The filter adaptation process is segmented into multiple blocks, allowing the constraining operation to be applied selectively rather than uniformly. This segmentation enables complexity reduction in less critical blocks while maintaining convergence performance in important blocks.
Solution Approach 2:
Different levels of constraining operation are applied to different filter coefficient blocks based on their importance. Critical blocks receive full constraining treatment to maintain convergence, while less critical blocks use simplified operations to reduce overall complexity.
3Productivity
If parallel filter update blocks are used, then productivity is improved, but device complexity increases
Solution Approach 1:
The filter update process is divided into multiple independent parallel blocks, each handling specific filter coefficient updates. This segmentation enables simultaneous processing of different coefficient blocks, improving productivity while keeping each individual block relatively simple in structure.
Data Source
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AI summary
Partitioned block frequency domain adaptive filter device comprising: a frequency domain adaptive filter configured for filtering a frequency domain representation of a time domain input signal depending on a set of filter coefficients consisting of a plurality of blocks of filter coefficients in order to produce a filtered signal; a plurality of parallel arranged filter update blocks, each of the filter update blocks being configured for updating one of the blocks of filter coefficients based on an update signal gathered by a circular correlation of a block of the frequency domain representation signal and a frequency domain control signal comprising a representation of the filtered signal; wherein each of the filter update blocks comprises an adaptation module configured for executing an adaptation sequence comprising the steps of calculating an approximation of a constrained gradient update for the filter coefficients of the respective block of filter coefficients by applying an approximated constraining matrix having a lesser complexity than a constraining matrix to an unconstrained gradient update for the filter coefficients of the respective block of filter coefficients, wherein the unconstrained gradient update is derived from the update signal, and calculating a cumulative error introduced on the unconstrained gradient update by applying the approximated constraining matrix to the unconstrained gradient update; wherein each of the filter update blocks comprises a correction module configured for executing a correction sequence comprising the steps of calculating a corrected constrained gradient update for the filter coefficients of the respective block of filter coefficients by applying the frequency domain constraining matrix to a sum of the approximation of the constrained gradient update and the cumulative error.