Affine Projection Adaptive Algorithm Research Articles

Improved clipped affine projection adaptive algorithm

In this study, a modification to the affine projection adaptive filtering algorithm is proposed which is based on a three-level clipping of the input signal as applied to the weight update process. This clipping operation causes the input signal to be quantised into three levels, namely, 0, 1, and -1. By doing so, the proposed scheme, in addition to reducing the computational complexity, achieves an error convergence performance that is comparable to, or even at times better than that of the conventional affine projection algorithm (APA) for a certain range of the clipping threshold. The proposed adaptive algorithm is compared with some low complexity variants of APA with respect to the convergence rate and computational complexity, and its superiority to its counterparts of the same order of complexity in terms of the convergence rate is demonstrated. The mean square error analysis based on the energy conservation relation and the stability analysis is also presented for the proposed clipped APA.

Acoustic echo cancellation using adaptive filtering algorithms for Quranic accents (Qiraat) identification

Echoed parts of Quranic accent (Qiraat) signals are exposed to reverberation of signals especially if they are listened to in a conference room or the Quranic recordings found in different media such as the web. Quranic verse rules identification/Tajweed are prone to additive noise and may reduce classification results. This research work aims to present our work towards Quranic accents (Qiraat) identification, which emphasizes on acoustic echo cancellation (AEC) of all echoed Quranic signals during the preprocessing phase of the system development. In order to conduct the AEC, three adaptive algorithms known as affine projection (AP), least mean square (LMS), and recursive least squares (RLS) are used during the preprocessing phase. Once clean Quranic signals are produced, they undergo feature extraction and pattern classification phases. The Mel Frequency Cepstral Coefficients is the most widely used technique for feature extraction and is adopted in this research work, whereas probabilities principal component analysis (PPCA), K-nearest neighbor (KNN) and gaussian mixture model (GMM) are used for pattern classification. In order to verify our methodology, audio files have been collected for Surat Ad-Duhaa for five different Quranic accents (Qiraat), namely: (1) Ad-Duri, (2) Al-Kisaie, (3) Hafs an A'asem, (4) IbnWardan, and (5) Warsh. Based on our experimental results, the AP algorithm achieved 93.9 % accuracy rate against all pattern classification techniques including PPCA, KNN, and GMM. For LMS and RLS, the achieved accuracy rates are different for PPCA, KNN, and GMM, whereby LMS with PPCA and GMM achieved the same accuracy rate of 96.9 %; however, LMS with KNN achieved 84.8 %. In addition, RLS with PPCA and GMM achieved the same accuracy rate of 90.9 %; however, RLS with KNN achieved 78.8 %. Therefore, the AP adaptive algorithm is able to reduce the echo of Quranic accents (Qiraat) signals in a consistent manner against all pattern classification techniques.

An affine projection algorithm with variable step size and projection order

It is known that the performance of adaptive algorithms is constrained by their computational cost. Thus, affine projection adaptive algorithms achieve higher convergence speed when the projection order increases, which is at the expense of a higher computational cost. However, regardless of computational cost, a high projection order also leads to higher final error at steady state. For this reason it seems advisable to reduce the computational cost of the algorithm when high convergence speed is not needed (steady state) and to maintain or increase this cost only when the algorithm is in transient state to encourage rapid transit to the permanent regime. The adaptive order affine projection algorithm presented here addresses this subject. This algorithm adapts its projection order and step size depending on its convergence state by simple and meaningful rules. Thus it achieves good convergence behavior at every convergence state and very low computational cost at steady state.

Robust Source Separation with Simple One-Source-Active Detection

Assuming there are short time periods in which only one source is active, a new approach for source separation is proposed. An affine projection adaptation algorithm with a non-orthogonal constraint shows excellent noise immunity, a high convergence rate, and good tracking capability to efficiently obtain a solution to the separation filters.

Fast Affine Projection Adaptation Algorithms With Stable and Robust Symmetric Linear System Slovers

This paper proposes two noniterative approaches to solve a symmetric linear system associated with the fast affine projection adaptation algorithm. The first approach, using matrix LDL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> factorization, can provide an exact solution at a moderate complexity, in contrast with the fact that existing stable approaches are all approximate. Based on a reciprocating recursion scheme, the second approach has a very low complexity and gives a good approximate solution. Steady-state and transient properties of the proposed and certain previous FAP algorithms are studied in detail. Being stable and optimal under all step size conditions, fast affine projection algorithms incorporating the proposed approaches are promising in telecom and other applications of adaptive filtering

Space-Time Adaptive MMSE Multiuser Receiver Structures for Wideband CDMA Systems

In this paper we propose computationally efficient space-time multiuser adaptive minimum mean square error (MMSE) receivers using affine projection adaptive (APA) filtering algorithm for asynchronous wideband code division multiple access systems (W-CDMA) in multipath channels. It is found that APA algorithm outperforms normalized least mean square (NLMS) algorithm in convergence characteristics with significant reduction in computational complexity as compared to recursive least square (RLS) algorithm. Three adaptive multiuser receiver architectures are considered, all employ chip-matched filter (CMF) at their front end. In the first two architectures perfect estimation of time delay of individual user is assumed and one CMF is employed per user. The difference between two architectures is that, in the first the samples collected from CMFs are cross-coupled and in the second architecture samples collected for individual users are not combined. In the third architecture, there is no need to estimate the time delay, but fractional sampling is used. It is shown that first architecture outperforms the other two architectures by virtue of its diversity advantage. It is also shown that by exploiting the spatial diversity (using multiple antennas at the receiver) significant improvement can be achieved in the output signal-to-interference-and-noise ratio (SINR).