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

Abstract


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.


Keywords


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

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References


K. R. Borisagar et al., “Speech Enhancement Techniques for Digital Hearing Aids”, Springer Nature Switzerland AG, 2019.

Saeed V. Vaseghi, “Advanced Digital Signal Processing and Noise Reduction”, John Wiley & Sons, Inc. January 2006.

Jingdong Chen et al., “Filtering Techniques for Noise Reduction and Speech Enhancement, Adaptive Signal Processing”, Springer, Berlin, Heidelberg, pp. 129-154, 2003.

B. Moons et al., “Embedded Deep Learning”, Springer Nature Switzerland AG, 2019

M. Tanveer et al., “Machine Intelligence and Signal Analysis”, Springer Verlag, Singapore, 1st ed, 2019

J. Kapoor, G. R. Mishra, and M. Rai, “Adaptive Least Mean Square Noise Cancellation Model Using Various Fixed Coefficient Digital Filters .,” Int. J. Adv. Sci. Technol., vol. 29, no. 10, pp. 8448–8455, 2020.

T.J.Moir, “Adaptive crosstalk-resistant noise-cancellation using H infinity filters”, IEEE International Conference on Signals and Systems (ICSigSys), pp. 123-456, 2019.

M. T. Akhtar, “An adaptive algorithm, based on modified tanh non-linearity and fractional processing, for impulsive active noise control systems,” J. Low Freq. Noise, Vib. Act. Control, vol. 37, no. 3, pp. 495–508, Sep. 2018, doi: 10.1177/1461348417725952.

Chang Liu et al., “Robust Adaptive Filter with Lncosh Cost”, Signal Processing, volume 168, march, 107348, 2020.

Rachana Nagal et al., “An Optimal Approach for Eeg/Erp Noise Cancellation Using Adaptive Filter with Oppositional Whale Optimization Algorithm”, Biomedical Engineering: Applications, Basis and Communications, Vol. 31, No. 05, 1950035, 2019.

D Niranjan et al., “Noise cancellation in musical signals using adaptive filtering algorithms”, International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) , pp. 82- 86, 2017.

Aniket Kumar et al., “Comparative research of various adaptive algorithms for noise cancellation in speech signals”, International Conference on Control, Computing, Communication and Materials (ICCCCM) , pp. 1- 5, 2016.

Azeddine Wahbi, Ahmed Roukhe, Laamari Hlou, “Modeling and Real-Time DSK C6713 Implementation of Normalized Least Mean Square (NLMS) Adaptive Algorithm for Acoustic Noise Cancellation (ANC) In Voice Communications”, (JATIT) Journal of Computer Technology & Applications,Vol. 65, No.2, pp. 312-319, 2014.

Azeddine Wahbi, Ahmed Roukhe, Laamari Hlou, “Conception and Real Time DSK C6713 of a Low Cost Adaptive Acoustic Noise Cancellation (ANC) Based Fast Fourier Transform (FFT) and Circular Convolution for Improving the Quality of Voice Communications”, (IJCTA) International Journal of Computer Technology & Applications, Vol 5, No.2, pp. 630-639, 2014.

Azeddine Wahbi et al., “Modeling and Simulation of Recursive Least Square Adaptive (RLS) Algorithm for Noise Cancellation in Voice Communication”, (JCC) Communication and Computer, Volume 10, issue 11. David Publishing Company, Vol. 11, pp. 1440-1444, 2013.

Sheng Zhang, Jiashu Zhang, Hing Cheung So, “Mean square deviation analysis of LMS and NLMS algorithms with white reference inputs”, Signal Processing ,131, pp. 20-26, 2017.

Rodrigo M. S. Pimenta, Leonardo C. Resende, Newton N. Siqueira, Diego B. Haddad, Mariane R. Petraglia, “A New Proportionate Adaptive Filtering Algorithm with Coefficient Reuse and Robustness Against Impulsive Noise”, 26th European Signal Processing Conference (EUSIPCO), pp. 470-474, 2018.

Minajul Haque, Kaustubh Bhattacharyya, A study on different linear and non-linear filtering techniques of speech and speech recognition, ADBU Journal of Engineering Technology(AJET), Volume 8, Issue 1, June, 008010606 (6PP), 2019

Maximilian Strake et al., “Fully Convolutional Recurrent Networks for Speech Enhancement, IEEE International Conference on Acoustics”, Speech and Signal Processing (ICASSP), pp. 6674-6678, 2020.

Han Zhao et al., “Convolutional-Recurrent Neural Networks for Speech Enhancement”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2401-2405, 2018.

Andrew Maas et al., Recurrent Neural Networks for Noise Reduction in Robust ASR”, 13th Annual Conference of the International Speech Communication Association, pp. 22-25, 2012.

Ke Tan et al., “A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement, Interspeech, pp. 3229- 3233, 2018.

Byoung-Tak Zhang, “An Incremental Learning Algorithm That Optimizes Network Size and Sample Size in One Trial”, IEEE International Conference on Neural Networks, Florida, pp. 215-220, 1994.

D. E. Rumelhart, G. E. Hilton, and R. J. Williams, “Learning representations by back-propagation errors”, Nature, 323, pp. 533-536, 1986.

Noman Q. Al-Naggar and Mohammed H. Al-Udyni, “Performance of Adaptive Noise Cancellation with Normalized Last-Mean-Square Based on the Signal-to-Noise Ratio of Lung and Heart Sound Separation”, Journal of Healthcare Engineering, Volume 2018.

G.K Rajini et al., “A Research on Different Filtering Techniques and Neural Networks Methods for Denoising Speech Signals”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) Volume-8, Issue-9S2, pp. 503- 513, 2019.

Kuan-Chun Chen et al, “Active Noise Control In a Duct to Cancel Broadband Noise”, 1st Nommensen International Conference on Technology and Engineering, IOP Conf. Series: Materials Science and Engineering, Volume 237, Issue 1, pp. 012015, 2017.

R. Ram and M. N. Mohanty, “Fractional DCT ADALINE method for speech enhancement,” 2017.




DOI: http://dx.doi.org/10.18517/ijaseit.11.2.13010

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