ARCHIVES
Original Article
Noise-Resilient Waveform Discrimination: Evaluating Kernel and Ensemble Methods Across Varying Signal-to- Noise Ratios
Swapnil Wanjare1
Dr. Vishwas Gaikwad2
1 2 Department of Electronics and Telecommunications, Sipna College of Engineering and Technology, Maharashtra, India.
Published Online: July-August 2026
Pages: 206-210
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260704022
References
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France, Tech. Rep., 2004.
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New York, NY, USA: Springer, 2009.
14. S. K. Mitra, Digital Signal Processing: A Computer-Based Approach, 4th ed. New York, NY, USA: McGraw-Hill, 2010.
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4, pp. 18–28, Jul./Aug. 1998.
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520–538, Apr. 1992.
18. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York, NY, USA: Wiley, 2001.
19. S. Haykin, Neural Networks and Learning Machines, 3rd ed. Upper Saddle River, NJ, USA: Pearson Education, 2009.
20. J. Myles, R. N. Feudale, Y. Liu, N. A. Woody, and S. D. Brown, "An introduction to decision tree modeling," J. Chemometrics, vol.
18, no. 6, pp. 275–285, Jun. 2004.
21. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th Annu. Workshop Comput.
Learning Theory (COLT), Pittsburgh, PA, USA, 1992, pp. 144–152.
22. S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, "1D convolutional neural networks and applications:
A survey," Mech. Syst. Signal Process., vol. 151, p. 107398, Feb. 2021.
23. G. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed. Burlington, MA, USA: Academic Press, 2008.
24. H. Nuttall, "Some windows with very good sidelobe behavior," IEEE Trans. Acoust., Speech, Signal Process., vol. 29, no. 1, pp. 84–91,
Feb. 1981.
25. F. J. Harris, "On the use of windows for harmonic analysis with the discrete Fourier transform," Proc. IEEE, vol. 66, no. 1, pp. 51–83,
Jan. 1978
2. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 4th ed. Upper Saddle River,
NJ, USA: Pearson Prentice Hall, 2006.
3. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001.
4. V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer-Verlag, 1995.
5. P. Stoica and R. L. Moses, Spectral Analysis of Signals. Upper Saddle River, NJ, USA: Prentice Hall, 2005.
6. J. Mao, "Data of Simulation Signal Experiments," Kaggle, 2024. [Online]. Available:
https://kaggle.com/datasets/jiumingmao/data-of-simulation-signal-experiments
7. Pedregosa et al., "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825–2830, Oct. 2011.
8. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273–297, Sep. 1995.
9. J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Ann. Statist., vol. 29, no. 5, pp. 1189–1232, Oct. 2001.10. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data
Mining, San Francisco, CA, USA, 2016, pp. 785–794.
11. G. Peeters, "A large set of audio features for sound description (similarity and classification) in the CUIDADO project," IRCAM, Paris,
France, Tech. Rep., 2004.
12. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
13. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.
New York, NY, USA: Springer, 2009.
14. S. K. Mitra, Digital Signal Processing: A Computer-Based Approach, 4th ed. New York, NY, USA: McGraw-Hill, 2010.
15. Cohen, Time-Frequency Analysis. Englewood Cliffs, NJ, USA: Prentice-Hall, 1995.
16. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, "Support vector machines," IEEE Intell. Syst. Their Appl., vol. 13, no.
4, pp. 18–28, Jul./Aug. 1998.
17. Boashash, "Estimating and interpreting the instantaneous frequency of a signal—Part 1: Fundamentals," Proc. IEEE, vol. 80, no. 4, pp.
520–538, Apr. 1992.
18. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York, NY, USA: Wiley, 2001.
19. S. Haykin, Neural Networks and Learning Machines, 3rd ed. Upper Saddle River, NJ, USA: Pearson Education, 2009.
20. J. Myles, R. N. Feudale, Y. Liu, N. A. Woody, and S. D. Brown, "An introduction to decision tree modeling," J. Chemometrics, vol.
18, no. 6, pp. 275–285, Jun. 2004.
21. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th Annu. Workshop Comput.
Learning Theory (COLT), Pittsburgh, PA, USA, 1992, pp. 144–152.
22. S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, "1D convolutional neural networks and applications:
A survey," Mech. Syst. Signal Process., vol. 151, p. 107398, Feb. 2021.
23. G. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed. Burlington, MA, USA: Academic Press, 2008.
24. H. Nuttall, "Some windows with very good sidelobe behavior," IEEE Trans. Acoust., Speech, Signal Process., vol. 29, no. 1, pp. 84–91,
Feb. 1981.
25. F. J. Harris, "On the use of windows for harmonic analysis with the discrete Fourier transform," Proc. IEEE, vol. 66, no. 1, pp. 51–83,
Jan. 1978
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