Instantaneous Frequency Selective Filtering Using Ensemble Empirical Mode Decomposition

References

• B. Boashash. Time Frequency Signal Analysis and Processing: A Comprehensive Reference. Elsevier, Oxford, UK, 2003.
   Google Scholar
• N. Huang, Z. Shen, S. Long, M. Wu, H. Shih, Q. Zheng, N. Yen, C. Tung, and H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci, Vol. 454, pp. 903–95, 1998. doi: 10.1098/rspa.1998.0193
   Web of Science ®Google Scholar
• R. Gupta, A. Kumar, and R. Bahl, “Estimation of instantaneous frequencies using iterative empirical mode decomposition,” Signal Image Video P, Vol. 8, no. 5, pp. 799–812, 2014. doi: 10.1007/s11760-012-0305-5
   Web of Science ®Google Scholar
• Y. Chen, and S. Fomel, “EMD-seislet transform,” Geophysics, Vol. 83, no. 1, pp. A27–A32, 2018. doi: 10.1190/geo2017-0554.1
   Web of Science ®Google Scholar
• A. Kizilkaya, and M. D. Elbi, “Different scenarios on denoising of signals in the intrinsic mode function selection framework,” IETE. J. Res., Vol. 62, no. 5, pp. 605–14, Sep. 2016. doi: 10.1080/03772063.2015.1136576
   Web of Science ®Google Scholar
• M. Suchetha, and N. Kumaravel, “Empirical mode decomposition-based subtraction techniques for 50 Hz interference reduction from electrocardiogram,” IETE. J. Res., Vol. 59, no. 1, pp. 55–62, 2013. doi: 10.4103/0377-2063.110631
   Web of Science ®Google Scholar
• N. Castañeda-Villa, P. Granados-Trejo, and J. M. Cornejo-Cruz, “Characterization of intrinsic mode functions of the Electrical cochlear response,” in C.A.G. Díaz, C.C. González, E.L. Leber, H.A. Vélez, N.P. Puente, D.L. Flores, A.O. Andrade, H.A. Galván, F. Martínez, R. García and C.J. Trujillo (Eds.), Latin American Conference on Biomedical Engineering. Springer, Cancún, México, 2019, pp. 228–33.
   Google Scholar
• B. Karan, S. S. Sahu, and K. Mahto, “Parkinson disease prediction using intrinsic mode function based features from speech signal,” Biocybern. Biomed. Eng., Vol. 40, no. 1, pp. 249–64, 2019. doi: 10.1016/j.bbe.2019.05.005
   Web of Science ®Google Scholar
• P. K. M. Krishna, and K. Ramaswamy, “Single channel speech separation based on empirical mode decomposition and Hilbert transform,” IET Signal Process., Vol. 11, no. 5, pp. 579–86, 2017. doi: 10.1049/iet-spr.2016.0450
   Web of Science ®Google Scholar
• G. Wu, S. Fomel, and Y. Chen, “Data-driven time–frequency analysis of seismic data using non-stationary prony method,” Geophys. Prospect., Vol. 66, no. 1, pp. 85–97, 2017. doi: 10.1111/1365-2478.12530
   Web of Science ®Google Scholar
• J. Gilles, “Empirical wavelet transform,” IEEE Trans. Signal Proc., Vol. 61, no. 16, pp. 3999–4010, 2013. doi: 10.1109/TSP.2013.2265222
   Web of Science ®Google Scholar
• K. Dragomiretskiy, and D. Zosso, “Variational mode decomposition,” IEEE Trans. Signal Proc., Vol. 62, no. 3, pp. 531–44, 2014. doi: 10.1109/TSP.2013.2288675
   Web of Science ®Google Scholar
• Z. Wu, and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Adv. Adap. Data Anal., Vol. 1, no. 1, pp. 1–41, 2009. doi: 10.1142/S1793536909000047
   Google Scholar
• A. V. Oppenheim, and R. W. Schafer. Digital Signal Processing. Prentice-Hall, Englewood Cliffs, New Jersey, 1975.
   Google Scholar
• S. Haykin. Communication Systems, 4th ed. John Wiley & Sons, New York, 2001.
   Google Scholar
• P. Flandrin, P. Gonçalves, and G. Rilling, “EMD equivalent filter banks, from interpretation to applications,” in N. E. Huang, and S. S. P. Shen (Eds.), Hilbert-Huang Transform and Its Applications. World Scientific, Singapore, 2005, pp. 57–74.
   Google Scholar
• W. Kuang, Z. Yang, B. W.-K. Ling, C. Y.-F. Ho, and Q. Dai, “Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach,” Digit. Signal. Process., Vol. 45, pp. 36–54, 2015. doi: 10.1016/j.dsp.2015.06.004
   Web of Science ®Google Scholar
• G. Rilling, and P. Flandrin, “One or two frequencies? The empirical mode de-composition answers,” IEEE Trans. Signal Proc., Vol. 56, no. 1, pp. 85–95, 2008. doi: 10.1109/TSP.2007.906771
   Web of Science ®Google Scholar
• A. Potamianos, and P. Maragos, “Speech analysis and synthesis using an AM-FM modulation model,” Speech. Commun., Vol. 28, no. 3, pp. 195–209, 1999. doi: 10.1016/S0167-6393(99)00012-6
   Web of Science ®Google Scholar
• A. Leonov, I. Makarov, and V. Sorokin, “Frequency modulations in the speech signal,” Acoust. Phys., Vol. 55, no. 6, pp. 876–87, 2009. doi: 10.1134/S1063771009060220
   Web of Science ®Google Scholar
• J. Kominek, and A. Black. The CMU ARCTIC speech databases for speech synthesis research. Available: http://festvox.org/cmuarctic, accessed 1 Jun. 2018.
   Google Scholar
• L. R. Rabiner, and R. W. Schafer. Digital processing of speech signals. A. Oppenheim, Series Ed. Prentice-Hall, Englewood Cliffs, NJ, 1978.
   Google Scholar