[1] Bagherzadeh, S.A., & Salehi, M. (2021). Analysis of in-flight cabin vi-bration of a turboprop airplane by proposing a novel noise-tolerant signal decomposition method. Journal of Vibration and Control,28(17), 2226-2239,
https://doi.org/10.1177/10775463211007583.
[2] Boashash, B. (2015). Time-Frequency Signal Analysis and Processing-A Comprehensive Reference (2nd ed.). Academic Press, New York.
[5] Boudraa, A.O., Khaldi, K., Chonavel, T., Hadj-Alouane, M. T., & Komaty, A. (2020). Audio coding via EMD. Digital Signal Processing, 104, 102770,
https://doi.org/10.1016/j.dsp.2020.102770.
[6] Burrus, C.S., Frigo, M., & Johnson, G. S. (2018). Fast Fourier Transforms. Samurai Media Limited.
[7] Chen, X., Chen, H., Hu, Y., & Li, R. (2023). A statistical instantaneous frequency estimator for high-concentration time-frequency representation. Signal Processing, 204, 108825,
https://doi.org/10.1016/j.sigpro.2022.108825.
[9] Ge, H., Chen, G., Yu, H., Chen, H., & An, F. (2018). Theoretical Analysis of Empirical Mode Decomposition. Symmetry, 10(11), 623.
https://doi.org/10.3390/sym10110623.
[10] Ghosh, P.K. (2007). Speech Segmentation using Extrema-Based Signal Track Length Measure. IEEE International Conference on Acoustics, Speech, Signal Processing-ICASSP, Honolulu, HI, USA,
https://doi.org/10.1109/ICASSP.2007.367257.
[11] Gokcesu,K., & Gokcesu, H. (2021). Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection, Clustering. arXiv preprint, arXiv:2109.02082,
https://doi.org/10.48550/arXiv.2109.02082.
[12] Gumelar, A.B., Purnomo, M. H., Yuniarno, E. M., & Sugiarto, I. (20189). Spectral Analysis of Familiar Human Voice Based On Hilbert-Huang Transform. International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, Indonesia.
https://doi.org/10.1109/CENIM.2018.8710943.
[13] Huang, N.E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition, the Hilbert spectrum for nonlinear, non-stationary time series analysis. Royal Society, 454(1971), 903-995,
https://doi.org/10.1098/rspa.1998.0193.
[14] Joy, B.R., Amara, A., & Nakhmani, A. (2018). Transform with no Parameters Based on Extrema Points for Non-stationary Signal Analysis. Circuits Systems and Signal Processing, 37, 2535-2547,
https://doi.org/10.1007/s00034-017-0676-5.
[15] Larson, R., & Edwards, B. (2022). Calculus of a single variable: with CalcChat, Calcview. Cengage Learning.
[16] Lin, L., & Hongbing, J. (2009). Signal feature extraction based on an improved EMD method. Measurement, 42(5), 796-803,
https://doi.org/10.1016/j.measurement.2009.01.001.
[17] Lin, T., Zhang, Y., & Muller-Petke, M. (2019). Random Noise Suppression of Magnetic Resonance Sounding Oscillating Signal by Combining Empirical Mode Decomposition, Time-Frequency Peak Filtering. IEEE Access, 7, 79917 - 79926,
https://doi.org/10.1109/ACCESS.2019.2923689.
[18] Meignen, S., & Gumery, P. Y. (2007). Reconstruction of Finite Signal Derivatives From Multiscale Extrema Representations: Application to Transient Estimation, Signal Approximation. IEEE Transactions on Signal Processing, 55(4), 1554-1559,
https://doi.org/10.1109/TSP.2006.887570.
[19] Niu, X. D., Lu, L. R., Wang, J., Han, X. C., Li, X., & Wang, L. M. (2021). An Improved Empirical Mode Decomposition Based on Local Integral Mean, Its Application in Signal Processing. Mathematical Problems in Engineering, 2021, 1-30,
https://doi.org/10.1155/2021/8891217.
[20] Poovarasan, S., & Chandra, E. (2019). Speech Enhancement Using Sliding Window Empirical Mode Decomposition, Hurst-based Technique. Archives of Acoustics, 44(3), 429-437,
http://doi.org/10.24425/aoa.2019.129259.
[22] Ray, P., Lenka, R. K., & Biswal, M. (2019). Frequency mode identi cation using modi ed masking signal-based empirical mode decomposition. IET Gener. Transm. Distrib, 13(8), 1266-1276.
https://doi.org/10.1049/iet-gtd.2018.5527.
[23] Rzepka, D., & Miskowicz, M. (2013). Recovery of varying-bandwidth signal from samples of its extrema. Signal Processing: Algorithms, Architectures, Arrangements, Applications (SPA), Poznan, Poland, ISBN:978-83-62065-17-2.
[24] Seifpour, S. , Niknazar, H., Mikaeili, M., & Motie Nasrabadi, A. (2018). A New Automatic Sleep Staging System Based on Statistical Behavior of Local Extrema Using Single Channel EEG Signal. Expert Systems With Applications, 104, 277-293,
https://doi.org/10.1016/j.eswa.2018.03.020.
[25] Sharma, N.K., & Sreenivas, T. V. (2015). Event-triggered sampling using signal extrema for instantaneous amplitude, instantaneous frequency estimation. Signal Processing, 116, 43-54,
https://doi.org/10.1016/j.sigpro.2015.03.025.
[26] Souza, U.B., Escola, J. P. L. & Brito, L. C. (2022). A survey on Hilbert-Huang transform: Evolution, challenges and solutions. Digital Signal Processing, 120(C),103292-103299.
https://doi.org/10.1016/j.dsp.2021.103292.
[27] Thuc, V. C., & Lee, H. S. (2022). Partial Discharge (PD) Signal Detection, Isolation on High Voltage Equipment Using Improved Complete EEMD Method. Energies, 15(16), 5819,
https://doi.org/10.3390/en15165819.
[28] Van Fleet, P.J. (2011). Discrete Wavelet Transformations: An Elementary Approach with Applications. John Wiley and Sons Inc, University of St. Thomas
[29] Wang, J., Wei, Q., Zhao,L., Yu, T., & Han, R. (2018). An improved empirical mode decomposition method using second generation wavelets interpolation. Digital Signal Processing, 79, 164-174,
https://doi.org/10.1016/j.dsp.2018.05.009.
[30] Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(1), 1-41, https://doi.org/10.1142/S1793536909000047.