![](/images/paper-icn.png)
A Novel Adaptive ANC Algorithm for Removal of Background Noise in Speech Applications
Noise is an unsafe mechanical toxin that causes serious hearing misfortune in the working environment of every nation. The working people in the military, mining, development, printing, and saw factories tend to lose their hearing performance due to the adverse effects of noise generated by the machines. They undergo elevated levels of noise, with various machinery producing greater levels of noise measured in decibels. These sounds may cause major health problems that may not allow the person to work in such conditions. Algorithms like Least Mean Square (LMS), Normalized Least Mean Squared (NLMS), Filtered-x Least Mean Squared (FxLMS) and Filtered-x Normalized Least Mean Squared (FxNLMS) are frequently being used for noise cancellation. Moreover, these filters have instability and poor noise reduction; slow convergence also requires a greater number of filter taps and less performance to identify the unknown system in the Active Noise Canceller (ANC). In this paper, a Précised FxNLMS (P-FxNLMS) algorithm is introduced for an ANC. This algorithm consists of dual adaptive filters, an updated Variable Step Size (VSS), a delay in the primary path, a slight improvement in the on-line secondary path, and a modified filter step size when compared to an existing ANC system, with the purpose of minimizing the demerits of existing algorithms. Initially, the P-FxNLMS algorithm was tested with Additive White Gaussian Noise (AWGN) and later tested with real noises from the NOISEUS dataset to check the noise reduction performance. The increase in Signal to Noise Ratio (SNR) segmentation for P-FxNLMS is around 1.45 dB to 4.07 dB and 38.46 % to 73.68 % of the Mean Square Error (MSE) as compared to the algorithms available for different sounds with different SNR input levels. From the performance results of MSE and SNR improvement (SNRi), we found improvements compared with existing algorithms.
[1] Abbas N. and Kabudian J., “Speech Scrambling Based on Independent Component Analysis and Particle Swarm Optimization,” The International Arab Journal of Information Technology, vol. 14, no. 4, pp. 521-527, 2017. https://www.iajit.org/upload/files/Speech- Scrambling-based-on-Independent-Component- Analysis-and-Particle-Swarm-Optimization.pdf
[2] Belyi V. and Gan W., “A Combined Bilateral and Binaural Active Noise Control Algorithm for Closed-Back Headphones,” Applied Acoustics, vol. 160, pp. 107129, 2020. https://doi.org/10.1016/j.apacoust.2019.107129
[3] Chavalitsakulcha P. and Shahnavaz H., “The Need for a Participatory Conservation Programme for the Reduction of Noise Exposure to Thai Female Workers,” Asia Pacific Journal of Public Health, vol. 3, no. 4, pp. 310-314, 1989. DOI:10.1177/101053958900300412
[4] Chen G. and Liu Y., “Mechanisms of Noise- Induced Hearing Loss Potentiation by Hypoxia,” Hearing Research, vol. 200, no. 1-2, pp. 1-9, 2005. https://doi.org/10.1016/j.heares.2004.08.016
[5] Elliott S. and Nelson P., “Active Noise Control,” IEEE Signal Processing Magazine, vol. 10, no. 4, pp. 12-35, 1993. DOI:10.1109/79.248551
[6] Elliott S., Signal Processing for Active Control, Academic Press, 2001. https://books.google.jo/books?id=GklDOTI6ZLIC& printsec=copyright&hl=ar#v=onepage&q&f=false
[7] Fang Y., Zhu X., Liu H., and Gao Z., “Hybrid Fx- NLMS Algorithm for Active Vibration Control of Flexible Beam with Piezoelectric Stack Actuator,” in Proceedings of the International Conference on Life System Modeling and Simulation, and International Conference on Intelligent Computing for Sustainable Energy and Environment, Nanjing, pp. 273-281, 2017. https://doi.org/10.1007/978-981-10-6370-1_27
[8] Fekri-Ershad S., Fakhrahmad S., and Tajeripour F., “Impulse Noise Reduction for Texture Images Using Real Word Spelling Correction Algorithm and Local Binary Patterns,” The International Arab Journal of Information Technology, vol. 15, no. 6, pp. 1024-1030, 2018. https://iajit.org/portal/PDF/November%202018, %20No.%206/10939.pdf
[9] Fuller C., Elliott S., and Nelson P., “Active Control of Vibration, Academic Press, 1996. https://www.sciencedirect.com/book/9780122694 400/active-control-of-vibration
[10] Hansen C. and Snyder S., Active Control of Noise and Vibration, E and FN Spon, 1997. https://m.ciop.pl/CIOPPortalWAR/appmanager/ciop/mo bi?_nfpb=true&_pageLabel=P42601713191498050635 882&html_tresc_root_id=300007675&html_tresc_id=3 00007719&html_klucz=300007675&html_klucz_spis=
[11] Hu Y. and Loizou P., “Subjective Evaluation and Comparison of Speech Enhancement Algorithms,” Speech Communication, vol. 49, no. 7-8, pp. 588-601, 2007. https://doi.org/10.1016/j.specom.2006.12.006
[12] Kim D., Lee M., and Park P., “A Robust Online Secondary-Path Filter Active Noise Control System for Noisy Inputs and Impulsive Noises in Sparse Systems,” in Proceedings of the IEEE Asia Pacific Conference on Circuits and Systems, Bangkok, pp. 281-284, 2019. DOI:10.1109/APCCAS47518.2019.8953121
[13] Koike S., “A Class of Adaptive Step-Size Control Algorithms for Adaptive Filters,” IEEE Transactions on Signal Processing, vol. 50, no. 6, pp. 1315-1326, 2002. DOI:10.1109/TSP.2002.1003057
[14] Kuo S. and Morgan D., “Active Noise Control: A Tutorial Review,” Proceeding of the IEEE, vol. 87, no. 6, pp. 943-973, 1999. DOI:10.1109/5.763310
[15] Kuo S. and Morgan D., Active Noise Control Systems-Algorithms and DSP Implementations, John Wiley, 1996. https://dl.acm.org/doi/book/10.5555/553072
[16] Kwong R. and Johnston E., “A Variable Step Size A Novel Adaptive ANC Algorithm for Removal of Background Noise in Speech Applications 599 LMS Algorithm,” IEEE Transactions on Signal Processing, vol. 40, no. 7, pp. 1633-1642, 1992. DOI:10.1109/78.143435
[17] Luo Z., Shi D., and Gan W., “A Hybrid SFANC- FxNLMS Algorithm for Active Noise Control Based on Deep Learning,” IEEE Signal Processing Letters, vol. 29, pp. 1102-1106, 2022. DOI:10.1109/LSP.2022.3169428
[18] Luo Z., Shi D., Gan W., Huang Q., and Zhang L., “Performance Evaluation of Selective Fixed-Filter Active Noise Control Based on Different Convolutional Neural Networks,” in Proceedings of the INTER-NOISE and NOISE-CON Congress, Glasgow, pp. 999-1999, 2022. https://doi.org/10.3397/IN_2022_0228
[19] Meng H. and Chen S., “A Modified Adaptive Weight-Constrained FxLMS Algorithm for Feedforward Active Noise Control Systems,” Applied Acoustics, vol. 164, pp. 107227, 2020. https://doi.org/10.1016/j.apacoust.2020.107227
[20] Nelson P. and Elliott S., Active Control of Sound, Academic Press, 1992. https://www.amazon.com/Active-Control-Sound- P-Nelson/dp/0125154267
[21] Okpala N., “Knowledge and Attitude of Infantry Soldiers to Hearing Conservation,” Military Medicine, vol. 172, no. 5, pp. 520-522, 2007. https://doi.org/10.7205/MILMED.172.5.520.
[22] Park T., Kim D., and Park P., “A Filtered-x VSS- NSAF Active Noise Control Algorithm Robust to Impulsive Noise through the Application of Step- Size Scaler,” in Proceedings of the 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Chiang Rai, pp. 744-747, 2018. DOI:10.1109/ECTICon.2018.8619925
[23] Ravinchandra K., Fei T., and Yong L., “Active Noise Reduction Using LMS and FxLMS Algorithms,” in Proceedings of the International Conference on Computer Vision and Machine Learning, Andhra Pradesh, 2018. DOI:10.1088/1742-6596/1228/1/012064
[24] Saito N. and Sone T., “Influence of Modeling Error on Noise Reduction Performance of Active Noise Control Systems Using Filtered-x LMS Algorithm,” Journal of the Acoustical Society of Japan (E), vol. 17, no. 4, pp. 195-202, 1996. https://doi.org/10.1250/ast.17.195
[25] Shi D., Gan W., Lam B., and Shi C., “Two- Gradient Direction FXLMS: An Adaptive Active Noise Control Algorithm with Output Constraint,” Mechanical Systems and Signal Processing, vol. 116, pp. 651-667, 2019. https://doi.org/10.1016/j.ymssp.2018.06.062
[26] Song P. and Zhao H., “Filtered-x Least Mean Square/Fourth Algorithm for Active Noise Control,” Mechanical Systems and Signal Processing, vol. 120, pp. 69-82, 2019. https://doi.org/10.1016/j.ymssp.2018.10.009
[27] Tokhi O. and Veres S., Active Sound and Vibration Control: Theory and Applications, Institute of Electrical Engineers, 2002. http://eprints.soton.ac.uk/id/eprint/22542
[28] Vazquez A., Avalos J., Sanchez G., Sanchez J., and Perez H., “A Comparative Survey of Convex Combination of Adaptive Filters,” IETE Journal of Research, vol. 69, no. 2, pp. 940-950, 2020. https://doi.org/10.1080/03772063.2020.1844075
[29] Vinothkumar G. and Manoj Kumar D., “Speech Enhancement with Background Noise Suppression in Various Data Corpus Using Bi- LSTM Algorithm,” International Journal of Electrical and Electronics Research, vol. 12, no. 1, pp. 322-328, 2024. https://doi.org/10.37391/IJEER.120144
[30] Vinothkumar G. and Phani Kumar Polasi P. “Filter Performance of Sparse Noise for Controlling the Occurrence of Noise-Induced Hearing Loss Using Hybrid Algorithm,” in Proceedings of the 12th National Conference on Recent Advancements in Biomedical Engineering, Chennai, pp. 030013, 2020. https://doi.org/10.1063/5.0072454
[31] Wang X., Ou S., and Pang Y., “Adaptive Combination of Filtered-X NLMS and Affine Projection Algorithms for Active Noise Control,” in Proceedings of the Artificial Intelligence: 2nd CAAI International Conference, Beijing, pp. 15- 25, 2022. https://doi.org/10.1007/978-3-031- 20503-3_2
[32] Zhao T., Liang J., Zou L., and Zhang L., “A New FXLMS Algorithm with Offline and Online Secondary-Path Modeling Scheme for Active Noise Control of Power Transformers,” IEEE Transactions on Industrial Electronics, vol. 64, no. 8, pp. 6432-6442, 2017. DOI:10.1109/TIE.2017.2682043