最小均方自适应滤波器与递归神经网络滤波器噪声消除的比较研究

Aakriti Agrawal, Rohitkumar Arasanipalai, B. Sainath
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引用次数: 0

摘要

人工神经网络(ANN)中的算法正在发展成为各种电气工程应用特别是信号处理应用中传统算法的更好替代品。具体来说,我们专注于一种特殊类型的人工神经网络,称为递归神经网络(RNN),由于内部存储器的存在,它在序列数据上提供了卓越的性能。本文比较分析了RNN和LMS自适应滤波器对音频数据的主动降噪性能。我们使用归一化均方误差(NMSE)作为性能度量进行比较。此外,我们还通过数值模拟研究了训练的epoch数和给出期望输出所需的时间。仿真结果表明,RNN滤波器比传统LMS滤波器具有更好的NMSE性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparative Study of Noise Cancellation Using Least Mean Squares Adaptive Filter and Recurrent Neural Network Filter
Algorithms in artificial neural networks (ANN) are evolving as better alternatives to conventional algorithms applied in various electrical engineering applications in general and signal processing applications in particular. Specifically, we focus on a special type of ANN called recurrent neural networks (RNN), which delivers superior performance on sequential data due to the presence of internal memory. In the present paper, we comparatively analyze the performance of RNN and least mean squares (LMS) adaptive filter on audio data for active noise cancellation. We use normalized mean squared error (NMSE) as performance measure for comparison. Furthermore, we also investigate the number of epochs for training and the time taken to give the desired output via numerical simulations. Our simulations show that RNN filter delivers better NMSE performance than conventional LMS filter.
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