This thesis presents algorithm and architecture for
simultaneous diagonalization of matrices. As an
example, a subspace-based speech enhancement problem
is considered, where in the covariance matrices of
the speech and noise are diagonalized simultaneously.
In order to compare the system performance of the
proposed algorithm, objective measurements of speech
enhancement is shown in terms of the signal to noise
ratio and mean bark spectral distortion at various
noise levels. In addition, an innovative subband
analysis technique for subspace-based time-domain
constrained speech enhancement technique is proposed.
The proposed technique analyses the signal in its
subbands to build accurate estimates of the
covariance matrices of speech and noise, exploiting
the inherent low varying characteristics of speech
and noise signals in narrow bands. The subband
approach also decreases the computation time by
reducing the order of the matrices to be
simultaneously diagonalized. Further, architecture is
proposed to implement the simultaneous
diagonalization scheme. The architecture is
implemented on a Xilinx FPGA. FPGA resource
utilization re-enforces on the practicability of the
design.