The Second-order Sequential Best Rotation (SBR2) algorithm, used
for Eigenvalue Decomposition (EVD) on para-Hermitian polynomial
matrices typically encountered in wideband signal processing
applications like multichannel Wiener filtering and channel coding,
involves a series of delay and rotation operations to achieve diagonalisation.
In this paper, we proposed the use of Householder transformations
to reduce polynomial matrices to tridiagonal form before
zeroing the dominant element with rotation. Similar to performing
Householder reduction on conventional matrices, our method
enables SBR2 to converge in fewer iterations with smaller order
of polynomial matrix factors because more off-diagonal Frobeniusnorm
(F-norm) could be transferred to the main diagonal at every
iteration. A reduction in the number of iterations by 12.35% and
0.1% improvement in reconstruction error is achievable.