An Improved Variable Structure Adaptive Filter Design and Analysis for Acoustic Echo Cancellation

In this research an advance variable structure adaptive Multiple Sub-Filters (MSF) based algorithm for single channel Acoustic Echo Cancellation (AEC) is proposed and analyzed. This work suggests a new and improved direction to find the optimum tap-length of adaptive filter employed for AEC. The structure adaptation, supported by a tap-length based weight update approach helps the designed echo canceller to maintain a trade-off between the Mean Square Error (MSE) and time taken to attain the steady state MSE. The work done in this paper focuses on replacing the fixed length sub-filters in existing MSF based AEC algorithms which brings refinements in terms of convergence, steady state error and tracking over the single long filter, different error and common error algorithms. A dynamic structure selective coefficient update approach to reduce the structural and computational cost of adaptive design is discussed in context with the proposed algorithm. Simulated results reveal a comparative performance analysis over proposed variable structure multiple sub-filters designs and existing fixed tap-length sub-filters based acoustic echo cancellers

Efficient Adaptive Filter Algorithms Using Variable Tap-length Scheme

Today the usage of digital signal processors has increased, where adaptive filter algorithms are now routinely employed in mostly all contemporary devices such as mobile phones, camcorders, digital cameras, and medical monitoring equipment, to name few. The filter tap-length, or the number of taps, is a significant structural parameter of adaptive filters that can influences both the complexity and steady-state performance characteristics of the filter. Traditional implementation of adaptive filtering algorithms presume some fixed filter-length and focus on estimating variable filter\u27s tap-weights parameters according to some pre-determined cost function. Although this approach can be adequate in some applications, it is not the case in more complicated ones as it does not answer the question of filter size (tap-length). This problem can be more apparent when the application involves a change in impulse response, making it hard for the adaptive filter algorithm to achieve best potential performance. A cost-effective approach is to come up with variable tap-length filtering scheme that can search for the optimal length while the filter is adapting its coefficients. In direct form structure filtering, commonly known as a transversal adaptive filter, several schemes were used to estimate the optimum tap-length. Among existing algorithms, pseudo fractional tap-length (FT) algorithm, is of particular interest because of its fast convergence rate and small steady-state error. Lattice structured adaptive filters, on the other hand, have attracted attention recently due to a number of desirable properties. The aim of this research is to develop efficient adaptive filter algorithms that fill the gap where optimal filter structures were not proposed by incorporating the concept of pseudo fractional tap-length (FT) in adaptive filtering algorithms. The contribution of this research include the development of variable length adaptive filter scheme and hence optimal filter structure for the following applications: (1) lattice prediction; (2) Least-Mean-Squares (LMS) lattice system identification; (3) Recursive Least-Squares (RLS) lattice system identification; (4) Constant Modulus Algorithm (CMA) blind equalization. To demonstrate the capability of proposed algorithms, simulations examples are implemented in different experimental conditions, where the results showed noticeable improvement in the context of mean square Error (MSE), as well as in the context of convergence rate of the proposed algorithms with their counterparts adaptive filter algorithms. Simulation results have also proven that with affordable extra computational complexity, an optimization for both of the adaptive filter coefficients and the filter tap-length can be attained

Variable tap-length adaptive algorithm which exploits both second and fourth order statistics.

A new variable tap-length adaptive algorithm which exploits both second and fourth order statistics is proposed in this paper. In this algorithm, the tap-length of the adaptive filter is varying rather than fixed, and controlled by fourth order statistics, whereas the coefficient update retains a conventional least mean square (LMS) form. As will be seen in the simulation results, the proposed algorithm has a faster convergence rate as compared with an existing variable tap-length LMS algorithm which is based only on second order statistics in sub-Gaussian noise environments

Convex combination of adaptive filters for a variable tap-length LMS algorithm

A convex combination of adaptive filters is utilized to improve the performance of a variable tap-length least-mean-square (LMS) algorithm in a low signal-to-noise environment (SNR 0 dB). As shown by our simulations, the adaptation of the tap-length in the variable tap-length LMS algorithm is highly affected by the parameter choice and the noise level. Combination approaches can improve such adaptation by exploiting advantages of parallel adaptive filters with different parameters. Simulation results support the good properties of the proposed method

Steady-state performance analysis of a variable tap-length LMS algorithm

A steady-state performance analysis of the fractional tap-length (FT) variable tap-length least mean square (LMS) algorithm is presented in this correspondence. Based on the analysis, a mathematical formulation for the steady-state tap length is obtained. Some general criteria for parameter selection are also given. The analysis and the associated discussions give insight into the performance of the FT algorithm, which may potentially extend its practical applicability. Simulation results support the theoretical analysis and discussions

A new variable tap-length LMS algorithm to model an exponential decay impulse response

This letter proposes a new variable tap-length least-mean-square (LMS) algorithm for applications in which the unknown filter impulse response sequence has an exponential decay envelope. The algorithm is designed to minimize the mean-square deviation (MSD) between the optimal and adaptive filter weight vectors at each iteration. Simulation results show the proposed algorithm has a faster convergence rate as compared with the fixed tap-length LMS algorithm and is robust to the initial tap-length choice

Variable length adaptive filtering within incremental learning algorithms for distributed networks

In this paper we propose the use of variable length adaptive filtering within the context of incremental learning for distributed networks. Algorithms for such incremental learning strategies must have low computational complexity and require minimal communication between nodes as compared to centralized networks. To match the dynamics of the data across the network we optimize the length of the adaptive filters used within each node by exploiting the statistics of the local signals to each node. In particular, we use a fractional tap-length solution to determine the length of the adaptive filter within each node, the coefficients of which are adapted with an incremental-learning learning algorithm. Simulation studies are presented to confirm the convergence properties of the scheme and these are verified by theoretical analysis of excess mean square error and mean square deviation