Intelligent Noise Cancellation Using Adaptive RNN-Based Filtering (Comprehensive Journal of Science, 2025)
This study details the design and implementation of an Adaptive Noise Canceller (ANC) using a Recurrent Neural Network (RNN), especially a Nonlinear Autoregressive model with exogenous inputs (NARX).  The suggested approach is designed to efficiently remove non-stationary and colored noise from voice signals, a prevalent issue in settings like airplane cockpits.  The dynamic recurrent architecture of the NARX network facilitates the improved modelling of intricate noise patterns for precise prediction and cancellation.  The process was modelled in MATLAB/Simulink, showing that the RNN-based adaptive filter attains superior noise suppression, as shown by a low Root Mean Square (RMS) error. This method has been compared with the standard method. The findings validate that the recurrent network methodology provides outstanding accuracy, rendering it especially appropriate for applications where precision in noise cancellation is paramount, albeit it requires extended computational processing time.
Keywords: adaptive filter, dynamic neural network, NARX