20-04-2012, 12:12 PM
Blocks Implementation of Least Mean Square adaptiveFilter
Blocks_Implementation_of_Least_Mean_Square_adaptive,corrected.pptx (Size: 881.41 KB / Downloads: 46)
Echo in the telecommunication systems
Traditionally, echo cancellation has been seen as black magic, requiring a great deal of heuristic know-how and expertise to achieve acceptable practical results. However, through the development/implementation of a new adaptive filter scheme, echo cancellation can become a much easier task for today's design engineers.
The general solutions for reducing the additive echo noise are based on digital filtering process. The central part of any echo canceller is the adaptive filter. This filter builds a mathematical representation, or impulse response, of the echo path.
Echo is the repetition of a waveform due to reflection from points where the characteristics of the medium through which the wave propagates changes.
Echo is usefully employed in sonar and radar for detection and exploration purposes. In telecommunication, echo can degrade the quality of service, and echo cancellation is an important part of communication systems.
Acoustic echo and The discomfort caused by the echo
Before presenting the different techniques for treatment of acoustic echo, it is important to understand under what conditions the echo is perceived as an annoying disturbance.
The acoustic echo is present so embarrassing for 30ms overall transmission delay. This delay is well beyond whether in the context of hands-free telephony where the transmission delay is around 180 ms.
In the case of single talk, the echo cancellation system must provide an echo reduction of about 24 dB for delays lower than 25 ms and of about 40 dB for delays exceeding 25 ms. .
Adaptive Filters
the output of the FIR filter at time K is determined by the sum of the products between the tap weight vector, and K time delayed input values. These time delayed inputs are expressed in vector form by the column vector
Numerical Results
The BLMS algorithm is investigated in single-talk situation where empirical value for the step size is chosen as The dimension of the computed block in filter processing is taken N=64, L=192, which corresponds to a 24 ms impulse response for a sampling rate of 8 kHz. In practice, the length of the echo-path impulse response in the telephone network usually varies between about 4 ms and 64 ms and the sampling rate is usually 8 kHz.
Conclusion
numerical simulations have been conducted to evaluate the performances of the FFT-based Block-LMS adaptive. The criteria used to evaluate the performance of the BLMS algorithm using Fast Fourier Transform, were the filter weight error convergence(Nm) and the (ERLEC)Moreover, the comparison between the two algorithms, LMS and BLMS, was that of the execution time. The result observed, showed that the procedure for the realization of adaptive filter using blocks structure (BLMS algorithm), compared to the LMS algorithm, reduce the execution time when the length of the block increases.