Abstract: This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that completely removes the two kinds of delays and has a low complexity. The performance advantages and limitations of each algorithm are discussed based on an extensive evaluation, and the complexities are evaluated in terms of both the peak and average complexities.
Abstract: This paper presents an efficient capacity control algorithm for prediction error expansion based audio reversible data hiding. Current state-of-the-art audio reversible data hiding schemes use a simple capacity control algorithm that was first developed for image reversible data hiding. The performance of this algorithm can be improved by using a simple two threshold based approach. The two threshold approach can be easily integrated into any prediction error expansion based framework. Experimental results are provided for two such frameworks.
Abstract: In this paper, a new numerical efficient multichannel Wiener filter method for two-microphone behind the ear digital hearing aids, based on an approximation of the autocorrelation matrix is proposed. It is shown that, due to the use of noise reduction and active noise control, similar intelligibility improvements are obtained at greatly reduced overall numerical complexity.
Abstract: The recursive least-squares (RLS) algorithm should be explicitly regularized to achieve a satisfactory performance when the signal-to-noise ratio is low. However, a direct implementation of the involved matrix inversion results in a high complexity. In this paper, we present a recursive approach to the matrix inversion of the dynamically regularized RLS algorithm by exploiting the special structure of the correlation matrix. The proposed method has a similar complexity to the standard RLS algorithm. Moreover, the new method provides an exact solution for a fixed regularization parameter, and it has a good accuracy even for a slowly time-varying regularization parameter. Simulation results confirm the effectiveness of the new method.
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