Strategies for Productive Execution of Digital FIR Practical Filtering
Keywords:
Digital filters, Spectral analysis, Finite impulse response, Compensators, Impulse response
Abstract
With the huge development of communications technologies today, digital finite impulse response (FIR) filters have been widely utilized frameworks, waveform handling, and electronic frameworks implementations. The FIR filters impulse response experiences a sharp and unexpected deviation bringing about poor ghostly qualities in the frequency space of the planned channel. This sharp rot in the impulse response of the FIR digital channel is brought about by the low request of the examined channel set to unpredictable and differing upsides of the digital channel coefficients. In this exploration, a creative procedure was contemplated, tried, and implemented utilizing a digital channel compensator to enlighten the transient impact in the impulse response of a FIR channel. The suggested technique will lightly collect a fragmentary qualities to the FIR filter weights so the sudden regression will be limited beyond expanding the need for the filter. This will upgrade the next otherworldly qualities of the planned FIR digital channel and the perfect spectral components would be accomplished utilizing a more honed spectrum dismissal frequency range. The reproduction software has been employed using MatLab2020 Simulink Tool Box © with LPF fifth as well as tenth request digital filter plan.
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References
1. Nambiar, R., Tang, C.K.K. and Mars, P. (1992) ‘Genetic and learning automata algorithms for adaptive digital filters’, Proc. ICASSP 1992, Vol. 4, pp.41–44.
2. Ingber, L. (1996) ‘Adaptive simulated annealing (ASA): lessons learned’, J. Control and Cybernetics, Vol. 25, pp.33–54.
3. A.K.M. Fazlul Haque, “Enviable Parameters Extraction and Enhanced Performance of Digital Filters using FDA Tool”, Journal of Bangladesh Electronic Society 9 (1), 2009.
4. Mohammad Saiful Islam, Shaila Shanjada and Mithun Banik, “Improved Feature Extraction of Digital Filters using FDA Tool”, Thesis paper, Department of Electronics & Telecommunication Engineering, Daffodil International University, 2010.
5. Yaduvir Sing, Sweta Tripathi and Manoj Pandey, “Analysis of Digital IIR Filters with LabVIEW”, International Journal of Computer Applications, vol 10, 2010.
6. ZHANG Chengliang and WANG Aihong, “IIR digital filter design research and simulation on MATLAB”, 4th International Conference on Signal Processing Systems (ICSPS), 2012
7. Ethan Elenberg, Stephanie Ng, Anthony Hsu, Alaap Parikh, Michelle Yu, Marc L’Heureux and E.J. Thiele, “Digital Filter Design for Audio Processing”, American journal of physics, 1965.
8. Er. Daljit Singh Bajwa, Er.Karamjeet Singh and Navpreet Kaur Chahal, “Survey Paper of Digital IIR Filter Design”, International Journal of Advance Research in Computer Science and Software Engineering (IJARCSSE), 2014.
9. Ekta Yadav and Rupali, “Digital IIR Filter Design”, International Journal of Innovative Research in Technology (IJIRT), vol 1,2015
10. J. Engel, L. H. Hantrakul, C. Gu, and A. Roberts, “Ddsp: Differentiable digital signal processing,” in International Conference on Learning Representations, 2020.
11. P. Bhattacharya, P. Nowak, and U. Z¨olzer, “Optimization of cascaded parametric peak and shelving filters with back propagation algorithm,” in Digital Audio Effects (DAFx) 2020, Vienna, Austria, September 2020.
12. S. Nercessian, “Neural parametric equalizer matching using differentiable biquads,” in Digital Audio Effects (DAFx) 2020, Vienna, Austria, September 2020.
13. A. D. Back and A. C. Tsoi, “FIR and IIR synapses, a new neural network architecture for time series modeling,” Neural Computation, vol. 3, no. 3, pp. 375–385, 1991.
14. N. Allakhverdiyeva, “Application of neural network for digital recursive filter design,” in 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), October 2016, pp. 1–4.
15. V. V¨alim¨aki and J. R¨am¨o, “Neurally controlled graphic equalizer,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, pp. 1–1, 2019.
16. Yao, W., Chen, S., Tan, S. and Hanzo, L. (2009) ‘Particle swarm optimization aided minimum bit error rate multiuser transmission’, Proc. ICC 2009, Dresden, Germany, 14–18 June, 5 pages.
17. Balteanu, F. Linear Front End Module for 4G/5G LTE Advanced Applications. In Proceedings of the 2018 48th European Microwave Conference (EuMC), Madrid, Spain, 23–27 September 2018; pp. 251–254.
18. Kennedy, J. and Eberhart, R. (1995) ‘Particle swarm optimization’, Proc. 1995 IEEE Int. Conf. Neural Networks, Perth, Australia, 27 November–1 December, Vol. 4, pp.1942–1948.
19. Soliman, H.M., Awadallah, M.A. and Nadim Emira, M. (2008) ‘Robust controller design for active suspensions using particle swarm optimization’, Int. J. Modeling, Identification and Control, Vol. 5, No. 1, pp.66–76.
20. Das, S. and Konar, A. (2007) ‘A swarm intelligence approach to the synthesis of two-dimensional IIR filters’, Engineering Applications of Artificial Intelligence, Vol. 20, No. 8, pp.1086–1096
21. Fang, W., Sun, J. and Xu, W-B. (2006) ‘Design IIR digital filters using quantum-behaved particle swarm optimization’, Proc. 2nd Int. Conf. Natural Computation, Xian, China, 24–28 September, Part II, pp.637–640.
22. D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” in 3rd International Conference for Learning Representations, San Diego, 2015, 2015.
23. De Luca C J, Gilmore L D, Kuznetsov M and Roy S H 2010 Filtering the surface EMG signal: Movement artifact and baseline noise contamination Journal of Biomechanics 43 pp1573–1579
24. G. Pepe, L. Gabrielli, S. Squartini, and L. Cattani, “Evolutionary tuning of filters coefficients for binaural audio equalization,” Applied Acoustics, vol. 163, p. 107204, 2020.
25. “Designing audio equalization filters by deep neural networks,” Applied Sciences, vol. 10, no. 7, p. 2483, 2020.
26. O. Kirkeby, P. A. Nelson, H. Hamada, and F. Orduna-Bustamante, “Fast DE convolution of multichannel systems using regularization,” IEEE Transactions on Speech and Audio Processing, vol. 6, no. 2, pp. 189–194, march 1998.
27. G. Ramos and J. J. L´opez, “Filter design method for loudspeaker equalization based on iir parametric filters,” Journal of the Audio Engineering Society, vol. 54, no. 12, pp. 1162–1178, December 2006.
28. H. Behrends, A. Von dem Knesebeck, W. Bradinal, P. Neumann, and U. Z¨olzer, “Automatic equalization using parametric IIR filters,” Journal of the Audio Engineering Society, vol. 59, no. 3, pp. 102–109, march 2011.
29. G. Pepe, L. Gabrielli, S. Squartini, L. Cattani, and C. Tripodi, “Gravitational search algorithm for ir filter-based audio equalization,” in 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, 2020.
2. Ingber, L. (1996) ‘Adaptive simulated annealing (ASA): lessons learned’, J. Control and Cybernetics, Vol. 25, pp.33–54.
3. A.K.M. Fazlul Haque, “Enviable Parameters Extraction and Enhanced Performance of Digital Filters using FDA Tool”, Journal of Bangladesh Electronic Society 9 (1), 2009.
4. Mohammad Saiful Islam, Shaila Shanjada and Mithun Banik, “Improved Feature Extraction of Digital Filters using FDA Tool”, Thesis paper, Department of Electronics & Telecommunication Engineering, Daffodil International University, 2010.
5. Yaduvir Sing, Sweta Tripathi and Manoj Pandey, “Analysis of Digital IIR Filters with LabVIEW”, International Journal of Computer Applications, vol 10, 2010.
6. ZHANG Chengliang and WANG Aihong, “IIR digital filter design research and simulation on MATLAB”, 4th International Conference on Signal Processing Systems (ICSPS), 2012
7. Ethan Elenberg, Stephanie Ng, Anthony Hsu, Alaap Parikh, Michelle Yu, Marc L’Heureux and E.J. Thiele, “Digital Filter Design for Audio Processing”, American journal of physics, 1965.
8. Er. Daljit Singh Bajwa, Er.Karamjeet Singh and Navpreet Kaur Chahal, “Survey Paper of Digital IIR Filter Design”, International Journal of Advance Research in Computer Science and Software Engineering (IJARCSSE), 2014.
9. Ekta Yadav and Rupali, “Digital IIR Filter Design”, International Journal of Innovative Research in Technology (IJIRT), vol 1,2015
10. J. Engel, L. H. Hantrakul, C. Gu, and A. Roberts, “Ddsp: Differentiable digital signal processing,” in International Conference on Learning Representations, 2020.
11. P. Bhattacharya, P. Nowak, and U. Z¨olzer, “Optimization of cascaded parametric peak and shelving filters with back propagation algorithm,” in Digital Audio Effects (DAFx) 2020, Vienna, Austria, September 2020.
12. S. Nercessian, “Neural parametric equalizer matching using differentiable biquads,” in Digital Audio Effects (DAFx) 2020, Vienna, Austria, September 2020.
13. A. D. Back and A. C. Tsoi, “FIR and IIR synapses, a new neural network architecture for time series modeling,” Neural Computation, vol. 3, no. 3, pp. 375–385, 1991.
14. N. Allakhverdiyeva, “Application of neural network for digital recursive filter design,” in 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), October 2016, pp. 1–4.
15. V. V¨alim¨aki and J. R¨am¨o, “Neurally controlled graphic equalizer,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, pp. 1–1, 2019.
16. Yao, W., Chen, S., Tan, S. and Hanzo, L. (2009) ‘Particle swarm optimization aided minimum bit error rate multiuser transmission’, Proc. ICC 2009, Dresden, Germany, 14–18 June, 5 pages.
17. Balteanu, F. Linear Front End Module for 4G/5G LTE Advanced Applications. In Proceedings of the 2018 48th European Microwave Conference (EuMC), Madrid, Spain, 23–27 September 2018; pp. 251–254.
18. Kennedy, J. and Eberhart, R. (1995) ‘Particle swarm optimization’, Proc. 1995 IEEE Int. Conf. Neural Networks, Perth, Australia, 27 November–1 December, Vol. 4, pp.1942–1948.
19. Soliman, H.M., Awadallah, M.A. and Nadim Emira, M. (2008) ‘Robust controller design for active suspensions using particle swarm optimization’, Int. J. Modeling, Identification and Control, Vol. 5, No. 1, pp.66–76.
20. Das, S. and Konar, A. (2007) ‘A swarm intelligence approach to the synthesis of two-dimensional IIR filters’, Engineering Applications of Artificial Intelligence, Vol. 20, No. 8, pp.1086–1096
21. Fang, W., Sun, J. and Xu, W-B. (2006) ‘Design IIR digital filters using quantum-behaved particle swarm optimization’, Proc. 2nd Int. Conf. Natural Computation, Xian, China, 24–28 September, Part II, pp.637–640.
22. D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” in 3rd International Conference for Learning Representations, San Diego, 2015, 2015.
23. De Luca C J, Gilmore L D, Kuznetsov M and Roy S H 2010 Filtering the surface EMG signal: Movement artifact and baseline noise contamination Journal of Biomechanics 43 pp1573–1579
24. G. Pepe, L. Gabrielli, S. Squartini, and L. Cattani, “Evolutionary tuning of filters coefficients for binaural audio equalization,” Applied Acoustics, vol. 163, p. 107204, 2020.
25. “Designing audio equalization filters by deep neural networks,” Applied Sciences, vol. 10, no. 7, p. 2483, 2020.
26. O. Kirkeby, P. A. Nelson, H. Hamada, and F. Orduna-Bustamante, “Fast DE convolution of multichannel systems using regularization,” IEEE Transactions on Speech and Audio Processing, vol. 6, no. 2, pp. 189–194, march 1998.
27. G. Ramos and J. J. L´opez, “Filter design method for loudspeaker equalization based on iir parametric filters,” Journal of the Audio Engineering Society, vol. 54, no. 12, pp. 1162–1178, December 2006.
28. H. Behrends, A. Von dem Knesebeck, W. Bradinal, P. Neumann, and U. Z¨olzer, “Automatic equalization using parametric IIR filters,” Journal of the Audio Engineering Society, vol. 59, no. 3, pp. 102–109, march 2011.
29. G. Pepe, L. Gabrielli, S. Squartini, L. Cattani, and C. Tripodi, “Gravitational search algorithm for ir filter-based audio equalization,” in 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, 2020.
Published
2023-12-05
How to Cite
Karar H. Hussein. (2023). Strategies for Productive Execution of Digital FIR Practical Filtering. Central Asian Journal of Theoretical and Applied Science, 4(12), 8-25. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/1362
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