International Journal on Advanced Science, Engineering and Information Technology, Vol. 11 (2021) No. 2, pages: 525-530, DOI:10.18517/ijaseit.11.2.13010

A Robust Embedded Non-Linear Acoustic Noise Cancellation (ANC) Using Artificial Neural Network (ANN) for Improving the Quality of Voice Communications

Azeddine Wahbi, Ahmed Roukhe, Bahloul Bensassi, Laamari Hlou


Embedded Acoustic Noise Cancellation (ANC) has enjoyed remarkable success in the telecommunication field, and it becomes an essential component in various communications applications, such as digital transmission. So, it is an efficient method used to enhance the quality of communications against noise phenomena which is a problem in communication systems. This paper contributes towards a new non-linear embedded ANC based Artificial Neural Network (ANN) in digital signal processing and backpropagation (BP) of the gradient algorithm. This system is usually required for non-linear adaptive processing digital signals. The neuronal ANC estimates the noise path and subtracting noise from a received signal by minimizing a cost function. It is the mean square error. Thus, also the filter weights are adaptively updated. In this work, we designed and simulated our intelligent embedded ANC model with the help of MATLAB\Simulink software. The proposed system was designed by using embedded functions in Simulink. In addition, all simulation results are performed and verified using Signal Noise to Ratio (SNR) and Mean Square Error (MSE), number of iteration, neuronal architecture, criteria and it has been compared in various scenarios.  Finally, a study and analysis on convergence of neuronal ANC based backpropagation of the gradient algorithm demonstrate that our proposed system can effectively improve the quality of voice communications against the undesired noise. It also provides faster convergence during the back propagation of the gradient. Furthermore, the best values of SNR and MSE show the effectiveness of the proposed model.


Embedded systems; noise cancellation problem; artificial neural network; digital signal processing; back propagation.

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