A Family of Adaptive Filter Algorithms in Noise Cancellation for Speech Enhancement

TL;DR

This paper introduces two new adaptive filtering algorithms, the fast affine projection and fast Euclidean direction search, which improve noise cancellation in speech enhancement by balancing convergence speed and computational complexity.

Contribution

It presents novel adaptive filtering algorithms specifically designed for rapid noise cancellation in speech signals, offering better performance than traditional methods.

Findings

The new algorithms effectively attenuate noise in speech signals.

Simulation results show improved convergence and noise reduction.

The methods outperform existing adaptive filters in certain scenarios.

Abstract

In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering was presented, have a nice tradeoff between complexity and the convergence speed. This paper describes a new approach for noise cancellation in speech enhancement using the two new adaptive filtering algorithms named fast affine projection algorithm and fast Euclidean direction search algorithms for attenuating noise in speech signals. The simulation results demonstrate the good performance of the two new algorithms in attenuating the noise.