The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (vss-nlms) algorithm in the context of Echo Cancellation ...
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The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (vss-nlms) algorithm in the context of Echo Cancellation (EC). The core parameter in this algorithm is the Background Noise Power (BNP); in the estimation of BNP, the power difference between the desired signal and the filter output, statistically equaling to the error signal power, has been widely used in a rough manner. In this study, a precise BNP estimate is implemented by multiplying the rough estimate with a corrective factor, taking into consideration the fact that the error signal consists of background noise and misalignment noise. This corrective factor is obtained by subtracting half of the latest vss value from 1 after analyzing the ratio of BNP to the misalignment noise. Based on the precise BNP estimate, the Pvss-nlms algorithm suitable for the EC system is eventually proposed. In practice, the proposed algorithm exhibits a significant advantage of easier controllability application, as prior knowledge of the EC environment can be neglected. The simulation results support the preciseness of the BNP estimation and the effectiveness of the proposed algorithm.
Noise cancellation remains a significant challenge in signal processing, particularly when addressing non-stationary and time-varying noise sources. Traditional approaches, such as the Normalized Least Mean Square (NL...
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Noise cancellation remains a significant challenge in signal processing, particularly when addressing non-stationary and time-varying noise sources. Traditional approaches, such as the Normalized Least Mean Square (nlms) algorithm, are often limited by the fixed step size parameter, which dictates the trade-off between convergence rate and system robustness. In this study, an innovative Variable Step Size nlms (vss-nlms) algorithm is introduced, designed to dynamically adjust the step size parameter, thereby optimizing performance criteria including precision, robustness, convergence rate, and tracking ability. Employing system identification techniques within an adaptive filtering framework, this research advances the nlmsalgorithm by incorporating a variable step size parameter that adapts in real-time to the noise environment. The proposed vss-nlms algorithm is evaluated through extensive simulations, demonstrating a significant enhancement in the balance between Mean Square Error (MSE) reduction and convergence rate over both the conventional nlms and Recursive Least Squares (RLS) algorithms, whilst maintaining computational simplicity. In the context of adaptive filters, the vss-NLSM algorithm represents a substantial improvement for noise cancellation applications, particularly in scenarios characterized by variable noise dynamics. The results presented herein confirm that the vss-nlms algorithm not only achieves a superior trade-off between accuracy/robustness and convergence rate/tracking but also sets a new benchmark for adaptive noise cancellation strategies in complex acoustic environments.
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