The set of new techniques, summarily named FSAF, is faster in all respects: converging faster, taking less MIPS, having lower processing latency, etc. FSAF allows nesting / recomposing of the subband architecture, to facilitate efficient fast converging low-latency low MIPS applications for AEC, Dereverberation, Feedback Cancellation, ANC, etc.
The RLS Joint Time-Frequency initialization is discussed in detail. At this time, it appears to be the MA/FIR recursive generalization of kernel-based regularization a.k.a. kernel methods a.k.a. ReLS (see System Identification Toolbox for more details). Extensive simulations and some convergence Eigen spectrum-based analysis are in Part II. Part IV contains practical examples of real-life adaptive signal processing, with AEC converging within the first utterance before a word ends.
A new class of sub-optimal adaptive algorithms called Diagonal Least Squares (DLS) is introduced, to be integrated into audio applications. Meta-adaptive algorithms are discussed.
FSAF, besides usual per-subband processing, can be used to solve arbitrary high dimension system identification problems in a divide and conquer style, including Near Perfect Reconstruction Open Loop Delayless FSAF. For any predefined precision δ, FSAF solves M smaller, better-conditioned problems in subbands, using either RLS or diagonal/scalar step-size algorithms like Kaczmarz a.k.a. [N]LMS ((~1/(M + o(1/δp)): LMS; ~1/(M + o(1/δp))^2:RLS less MIPS vs full-band), and converts them back to the original full-band time domain with required precision δ.
Michael Zrull (2020). Fast Subband Adaptive Filtering (FSAF) (https://www.mathworks.com/matlabcentral/fileexchange/<...>), MATLAB Central File Exchange. Retrieved November 25, 2020.
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