Combined-Sample Multiband-Structured Subband Filtering Algorithms

Yishu Peng

doi:10.1109/TASLP.2022.3156791

Combined-Sample Multiband-Structured Subband Filtering Algorithms

2022-01-01

6

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AI

DOI：10.1109/TASLP.2022.3156791

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This paper introduces two combined-sample multiband-structured subband adaptive filters (MSAFs). In the design, an adaptive convex combination scheme of two self-reliant multi-sampled MSAF (MS-MSAF) with different sampled periods is firstly developed, which leads to the so-called CTMS-MSAF algorithm. Secondly, based on an adaptive filter, the combined-sample MS-MSAF (CMS-MSAF) algorithm is proposed via designing a time-varying sampled period, which possesses lower computational complexity than the former. Then, the convergence behaviors of the CTMS-MSAF and CMS-MSAF algorithms are investigated using standard mean-square deviation analysis. Finally, the simulation study in the system identification and acoustic echo cancellation applications shows that at the same steady-state error, the CMS-MSAF method provides a faster convergence rate than the improved convex combination of two MSAFs, combined-step-size MSAF and CTMS-MSAF algorithms.

Keyword:

Convergence

Steady-state

Optimized production technology

Speech processing

Time-domain analysis

Echo cancellers

Prediction algorithms

Adaptive filter

combined-sample

mean-square behavior

subband algorithm

Combined-Sample Multiband-Structured Subband Filtering Algorithms

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