Decomposition of long adaptive filter approach to acoustic echo cancellation and its implementation using FPGA | ||||
| The International Conference on Electrical Engineering | ||||
| Article 88, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Page 1-19 PDF (327.55 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2008.34335 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Amin Mohamed Nassar1; Ashraf Mohamed Ali2 | ||||
| 1Electronics &Communication Dept., Faculty of Engineering, Cairo University, Giza, Egypt. | ||||
| 2Modern Academy In Maadi, Communication Dept. | ||||
| Abstract | ||||
| 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. | ||||
| Keywords | ||||
| Decomposition; echo cancellation; FPGA; recourses; adaptive filter; steady state error; convergence rate and parallelism technique | ||||
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