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Hybrid method of speech signals spectral analysis based on the autoregressive model and Schuster periodogram

https://doi.org/10.32446/0368-1025it.2023-3-61-66

Abstract

The task of measuring the power spectral density of a speech signal in the regime of a sliding observation window is considered. A parametric approach to solving this task using an autoregressive data model is studied. The problem of optimizing the order of an autoregressive model under conditions of small samples is studied. The problem is proposed to be solved using a hybrid method of spectral analysis based on sequential enumeration of a fi nite number of alternatives. The optimization criterion is formulated in terms of the inverse problem: from a speech signal to a voice source. It uses the scaleinvariant measure of the spectral distance as the objective function, and the Schuster periodogram as the reference sample. The effectiveness of the hybrid method has been experimentally evaluated on the basis of author's software. It is shown that with the duration of the observation window of no more than 10 ms, the use of the hybrid method increases the accuracy of spectral analysis by more than 30 % compared to the well-known Berg method, the order of which is established according to the Akaike information criterion.

About the Author

V. V. Savchenko
National Research University Higher School of Economics
Russian Federation

Vladimir V. Savchenko

Nizhniy Novgorod



References

1. Marple S. L. Jr. Digital spectral analysis. 2-nd ed., Dover Publications, New York, 2019, 432 р.

2. Rabiner L. R., Shafer R. W. Theory and Applications of Digital Speech Processing. Boston, Pearson, 2010, 1060 p.

3. Savchenko V. V. Sovershenstvovanie metodiki izmereniya pokazatelya tochnosti avtoregressionnoj modeli rechevogo signala. Izmeritel’naya tekhnika, 2022, no. 10, рр. 58–63. (In Russ.) https://doi.org/10.32446/0368-1025it.2022-10-58-63

4. Savchenko, A. V., Savchenko V. V. Measurement Techniques, 2022, vol. 65, no. 6, pp. 453–460. https://doi.org/10.1007/s11018-022-02104-6

5. Savchenko V. V. Radioelectronics and Communications Systems, 2020, vol. 63, pp. 532–542. https://doi.org/10.3103/S0735272720100039

6. Ando Sh. The Journal of the Acoustical Society of America, 2019, vol. 146, 2846. https://doi.org/10.1121/1.5136873

7. Gu Yu., Wei H. L. Information Sciences, 2018, vol. 451–452, pp. 195–209. https://doi.org/10.1016/j.ins.2018.04.007

8. Liu Chu-An, Kuo Biing-Shen, Tsay Wen-Jen. Autoregressive Spectral Averaging Estimator. IEAS Working Paper, 2017, no. 17-A013, available at: https://www.econ.sinica.edu.tw/~econ/pdfPaper/17-A013.pdf (accessed: 02.02.2023).

9. Kuznetsov A. А. Measurement Techniques, 2014, vol. 57, no. 7, рр. 439–445. https://doi.org/10.1007/s11018-014-0474-4

10. Savchenko V. V., Savchenko L. V. Journal of Communications Technology and Electronics, 2021, vol. 66, no. 11, pp. 1266–1274. https://doi.org/10.1134/s1064226921110085

11. Mills T. C. Schuster, Beveridge and Periodogram Analysis. In: The Foundations of Modern Time Series Analysis. Macmillan, London. 2011, рp. 18–29. https://doi.org/10.1057/9780230305021_3

12. Kashin A. V., Kornev N. S., Makarichev N. A. et al. Instruments and Experimental Techniques, 2020, vol. 63, pp. 34–40. https://doi.org/10.1134/S0020441220010030

13. Savchenko V. V., Savchenko А. V. Radioelectronics and Communications Systems, 2019, vol. 62, no. 5, pp. 223–231. https://doi.org/10.3103/S0735272719050042

14. Ding J., Tarokh V., Yang Y. IEEE Transactions on Information Theory, 2018, vol. 64, no. 6, pp. 4024–4043. https://doi.org/10.1109/TIT.2017.2717599

15. Boisbunon A., Can S., Fourdrinier D., Strawderman W., Wells M. T. International Statistical Review, 2014, vol. 82, no. 3, pp. 422–439 https://doi.org/10.1111/insr.12052

16. Savchenko V. V. Radioelectronics and Communications Systems, 2021, vol. 64, pp. 592–603. https://doi.org/10.3103/S0735272721110030

17. Tohyama M. Spectral envelope and source signature analysis. In: Acoustic Signals and Hearing. Academic Press, 2020, pp. 89–110. https://doi.org/10.1016/B978-0-12-816391-7.00013-9

18. Radioelektronnye sistemy. Osnovy postroeniya i teoriya: Spravochnik, ed. Ya. D. Shirman. Moscow, Radiotekhnika Publ., 2007, 512 р. (In Russ.)

19. Savchenko A. V., Savchenko V. V., Savchenko L. V. Optimization Letters, 2022, no. 16, pp. 2095–2113. https://doi.org/10.1007/s11590-021-01790-5

20. Burg J. P. A New Analysis Technique for Time Series Data. In Modern Spectrum Analysis. IEEE Press, New York, 1978, 334 р.

21. Xiao D., Mo F., Zhang Ya., Zhao M., Ma L. Heliyon, 2018, vol. 4, no. 11, e00 948. https://doi.org/10.1016/j.heliyon.2018.e00948

22. Oppenheim A. V. Applications of Digital Signal Processing. Prentice-Hall, Englewood Cliffs, New Jersey, 1978, 510 р.


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For citations:


Savchenko V.V. Hybrid method of speech signals spectral analysis based on the autoregressive model and Schuster periodogram. Izmeritel`naya Tekhnika. 2023;(3):61-66. (In Russ.) https://doi.org/10.32446/0368-1025it.2023-3-61-66

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