Voice over IP (VoIP) applications are now very popular and widely used on the Internet. Such applications use receiver playout buffers to smooth delay variations so as to reconstruct the periodic form of the transmitted packets. Packets arriving after their scheduled playout time are considered late and are not played out. Playout delay control algorithms often operate by updating the playout d...

An algorithm is introduced that performs stereophonic acoustic echo cancellation (SAEC) for systems using pairwise panning of a single monophonic source to provide the effect of spatialisation. The technique exploits the inherent high correlation between the loudspeaker signals, unlike other general SAEC techniques, which try to utilise any small uncorrelated features in the signals. The algori...

Stochastic (on-line) learning can be faster than batch learning. However, at late times, the learning rate must be annealed to remove the noise present in the stochastic weight updates. In this annealing phase, the convergence rate (in mean square) is at best proportional to l/T where T is the number of input presentations. An alternative is to increase the batch size to remove the noise. In th...

This paper proposes a new leaky least mean square (leaky LMS, LLMS) algorithm in which a norm penalty is introduced to force the solution to be sparse in the application of system identification. The leaky LMS algorithm is derived because the performance ofthe standard LMS algorithm deteriorates when the input is highly correlated. However, both ofthem do not take the sparsity information into ...

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean...

Statistical analysis of the least mean-squares (LMS) adaptive algorithm with uncorrelated Gaussian datais presented. Exact analytical expressions for the steady-state mean-square error (mse) and the performance degradation due to weight vector misadjustment are derived. Necessary and sufficient conditions for the convergence of the algorithm to the optimal (Wiener) solution within a finite vari...

This paper presents a class of pipelined CORDIC architectures for the LMS-based transversal adaptive filter. For this, an alternate formulation of the LMS algorithm is considered, obtained by expressing the mean square error as a convex function of a set of angle variables that are monotonically related to the filter tap weights. The proposed architectures employ microlevel pipelining and are a...

Adaptive blind equalization has gained widespread use in communication systems that operate without training signals. In particular, the Constant Modulus Algorithm (CMA) has become a favorite of practitioners due to its LMS-like complexity and desirable robustness properties. The desire for further reduction in computational complexity has motivated signed-error versions of CMA, which have been...

The inherent feature of the Least Mean Squares (LMS) algorithm is the step size, and it requires careful adjustment. Small step size, required for small excess mean square error, results in slow convergence. Large step size, needed for fast adaptation, may result in loss of stability. Therefore, many modifications of the LMS algorithm, where the step size changes during the adaptation process d...

Current active noise control (ANC) technology cannot yield a balanced performance over broad frequency range when applied to powertrain noise control. It is because most of these ANC systems are configured with the traditional filtered-x least mean squares (FXLMS) algorithm with an inherent limitation in the frequency-dependent convergence behavior. In particular, the phase delay of the seconda...