PARTIAL UPDATE SUBBAND IMPLEMENTATION OF COMPLEX PSEUDO-AFFINE
PROJECTION ALGORITHM ON OVERSAMPLED FILTERBANKS

Fast adaptive algorithms targeted for low-resource
implementation on oversampled filterbanks are discussed. First,
simplifications to the Pseudo Affine Projection (PAP) are
proposed that decrease the computation cost without degrading
the performance. Next, the sequential update PAP algorithm is
proposed to further decrease the computational complexity. It is
shown that, unlike the partial update fast affine projection,
increasing the decimation rate of the partial update algorithm
does not lead to more aliasing in the sequential update PAP.
Moreover, it is experimentally observed that with proper
regularization or step-size scaling, the convergence rate of the
sequential PAP is identical for various decimation factors of the
partial update. The proposed methods are implemented and
evaluated on a low-resource oversampled filterbank platform.