The relationship between the number of extrema of compound sinusoidal signals and its high-frequency component

Document Type : Research Paper

Authors

1 RIV Lab., Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran

2 Department of Computer Engineering, Yazd University, Yazd, Iran

Abstract

As the main findings of our research work, we present a novel theorem on the relationship between the number of extrema of compound sinusoidal signals and its high-frequency component. In the case of signals consisting of the sum of two sine signals, if the high-frequency component has a higher product of the frequency and the amplitude, then we prove that the frequency of the high-frequency component is proportional to the number of extrema in a time interval. This theorem justifies some of the experimental results of other researchers on the relevance of extrema to frequency and amplitude. To confirm the theorem, extrema counting was performed on speech signals and compared with Fourier transform. The experimental results show that the average number of extrema of the compound sinusoidal signal or its derivatives over a time interval can be used to estimate the frequency at its highest frequency band. An important application of this research work is the fast calculation of high frequencies of a signal. This theorem also shows that the number of extrema points can be used as a new effective feature for signal processing, especially speech signals.

Keywords

Main Subjects


[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.
[3] Bojdi, Z.K., Hemmat, A. A., & Kebryaee, M. (2019). An Application of Daubechies Wavelet in Drug Release Model. Journal of Mahani Mathematical Research, 8(2), 53-68, https://doi.org/10.22103/jmmrc.2019.13914.1091.
[4] Boudraa, A.O., & Cexus, J.-C. (2007). EMD-Based Signal Filtering. IEEE Transactions on Instrumentation, Measurement, 56(6), 2196-2202, https://doi.org/10.1109/TIM.2007.907967.
[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.
[8] Daubechies, I. (1990). The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Transactions on Information Theory, 36(5), 961-1005, https://doi.org/10.1515/9781400827268.442.
[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.
[21] Premanand, B., & Sheeba, V. S. (2020). Compressed encoding of vibration signals using extremum sampling. SN Applied Sciences, 2(7), 1-10, https://doi.org/10.1007/s42452-020-3076-6.
[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.