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Table 3 Computational effort for the identification of a PBSA-HGM with a PB-FNLMS algorithm

From: Significance-aware filtering for nonlinear acoustic echo cancellation

Computed quantity Equation Multiplicity Required operations
Preprocessed input (30) \(p=0,\dots,\frac {N}{2}-1\) 0 0 0 (B−1)N/2 (B−1)N/2 0
HM submodel Table 2, B=1   2P+3 2P N N N N(2P−1) 6N N \(\tfrac {N}{2}+3N_{\mathrm {N}}\) N N
HGM submodel Table 2, P=1   3B+2 2B N N N N(2B−1)+N N 6B N N \(\frac {N}{2}+3BN_{\mathrm {N}}\) B N N
Kernel correlation (39) b 0 0 0 B N/2 B(N/2−1) 0
Instantaneous weights (38) b>1 0 0 0 0 0 B−1
Smoothing weights (40) b>1 0 0 0 2(B−1) B−1 0
   Accumulated: 3B+2P+5 2N N·(B+P) N N(2B+2P−1) \(\begin {aligned} 2B-\frac {N}{2}+\\ 6N_{\mathrm {N}}+BN+\\6BN_{\mathrm {N}}-2 \end {aligned}\) \(\begin {aligned} \frac {N}{2}+3N_{\mathrm {N}}+\\ BN+\\3BN_{\mathrm {N}}-1 \end {aligned}\) B+N N+B N N−1
    Total: \(\left (6B+4P+10\right)N\log _{2}N+8P-\frac {7N}{2}+B\left (\frac {21N}{2}+17\right)+5PN-16\) FLOPs