Abstract:
The modeling of the acoustic echo path was presented using multiple of small
adaptive filters rather than using one long adaptive filter. A new approach is proposed
using the concept of decomposing the long adaptive filter into low order multiple subfilters
in which the error signals are independent on each other. The independency of the
error signals exhibits the parallelism technique. This achieves our goal in increasing
speed of the convergence rate. Simulation results show that the proposed decomposed
least-mean-square (LMS) adaptive algorithm significantly improved the convergence
rate with respect to that of the original long adaptive filter. The proposed algorithm is
also compared with multiple sub-filters approach used for acoustic echo cancellation as
the technique of decomposition of error. This technique is based on using multiple subadaptive
filters in which the error signals are dependent on each other. In this way the
parallelism technique is not achieved and as the result the convergence rate increases.
This is different from our proposed technique which is based on independency of the
error signals to assure that our algorithm has faster convergence rate. The steady state
error of our proposed technique is still high as the technique of decomposition of error.
This steady state error is small with respect to using one long adaptive filter and this
will be obvious in our simulation results. The hardware implementation of this proposed
technique was also introduced using field programmable gate arrays (FPGAs). Filtering
data in real-time requires dedicated hardware to meet demanding time requirements. If
the statistics of the signal are not known, then adaptive filtering algorithms can be
implemented to estimate the signals statistics iteratively. The modeling of the acoustic
echo path was represented by using three sub-adaptive filters of order =10 with fixed
step size =0.05/3 for each adaptive filter. We use sinusoidal input signal with additive
white gaussian noise (AWGN) for different signal-to-noise ratio (SNR) to examine our
approach.