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An approach to implement LMS and NLMS adaptive noise cancellation algorithm in frequency domain

机译:一种在频域上实现LMS和NLMS自适应噪声消除算法的方法

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This paper presents the implementation of adaptive algorithms like Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) in the frequency domain and their comparison to that implemented in the time domain. Adaptive filtering using adaptive algorithm in frequency domain can be done by taking Fourier Transform of input signal and independent weigh coefficient. By frequency domain approach significant reduction in mathematical computation has been achieved. An expression for updating the weights is implemented in the frequency domain and statistical analysis has been performed. The SNR (Signal to Noise Ratio) is a parameter used to evaluate the performance with different step size. The signal power and noise power has also been calculated using MATLAB. The SNR of output signal rises about 8–9 times in frequency domain than the time domain.
机译:本文介绍了自适应算法(如最小均方(LMS)和归一化最小均方(NLMS))在频域中的实现,以及它们与时域实现的比较。通过采用输入信号的傅立叶变换和独立的加权系数,可以在频域中使用自适应算法进行自适应滤波。通过频域方法,已经实现了数学计算的显着减少。在频域中实现了用于更新权重的表达式,并且已经执行了统计分析。 SNR(信噪比)是用于评估不同步长的性能的参数。信号功率和噪声功率也已使用MATLAB计算。输出信号的SNR在频域中比时域上升约8–9倍。

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