Email this article A Method For Autoregression Modeling of a Speech Signal
- Authors: Savchenko V.V.1
-
Affiliations:
- National Research University Higher School of Economics
- Issue: Vol 68, No 2 (2023)
- Pages: 138-145
- Section: ТЕОРИЯ И МЕТОДЫ ОБРАБОТКИ СИГНАЛОВ
- URL: https://journals.rcsi.science/0033-8494/article/view/138112
- DOI: https://doi.org/10.31857/S0033849423020122
- EDN: https://elibrary.ru/LDATIU
- ID: 138112
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Abstract
The problem of autoregressive modeling of a speech signal based on the data of the discrete Fourier transform in the mode of a sliding observation window of small duration (milliseconds) is considered. The problem of stability of the formed autoregressive model is investigated. To overcome it, it is proposed to use the envelope of the Schuster periodogram as a reference spectral sample. A new method of autoregressive modeling has been developed, in which the detection of the spectral envelope is carried out using a recirculator of a sequence of samples in the frequency domain. An example of its practical implementation is considered, a full-scale experiment is set up and carried out. Based on the results of the experiment, conclusions were drawn about achieving a significant gain in terms of not only stability, but also the accuracy of the autoregressive model of the speech signal.
About the authors
V. V. Savchenko
National Research University Higher School of Economics
Author for correspondence.
Email: vvsavchenko@yandex.ru
Nizhny Novgorod, 603155 Russia
References
- Gibson J. // Entropy. 2018. V. 20. № 10. P. 7502018. https://doi.org/10.3390/e20100750
- Gudnason J. Speech Production Modeling and Analysis. Academic Press Library. In Signal Processing, Elsevier. 2014. V. 4. P. 985. https://doi.org/10.1016/B978-0-12-396501-1.00034-0
- Ando Sh. // The J. Acoustical Society of America. 2019. V. 146. P. 2846. https://doi.org/10.1121/1.5136873
- Cui S., Li E., Kang X. // IEEE Int. Conf. Multimedia and Expo (ICME). London. 06–10 Jul. 2020. N.Y.: IEEE, 2020. P. 9102765. https://doi.org/10.1109/ICME46284.2020.9102765
- Savchenko V.V. // Radioelectronics and Communications Systems. 2021. V. 64. № 11. P. 592. https://doi.org/10.3103/S0735272721110030
- Castanié F. Digital Spectral Analysis. Parametric, Non-Parametric and Advanced Methods. Hoboken–London: Wiley-ISTE. 2011. https://doi.org/10.1002/9781118601877
- Rabiner L.R., Shafer R.W. Theory and Applications of Digital Speech Processing. Boston: Pearson, 2010.
- Marple Jr. S.L. Digital Spectral Analysis with Applications. Mineola, N.Y.: Dover Publications, 2019.
- Савченко В.В., Савченко Л.В. // РЭ. 2021. Т. 66. № 11. С. 1100. https://doi.org/10.31857/S0033849421110085
- Kazemipour A., Miran S., Pal P. et al. // IEEE Trans. 2017. V. SP-65. № 9. P. 2333. https://doi.org/10.1109/TSP.2017.2656848
- Гоноровский И.С. Радиотехнические цепи и сигналы. М.: Сов. радио, 1977.
- Mustiere F., Bouchard M., Bolic M. // IEEE Trans. 2012. V. ASLP-20. № 2. P. 705. https://doi.org/10.1109/TASL.2011.2163511
- Savchenko A.V., Savchenko V.V. // Radioelectronics and Communications Systems. 2021. V. 64. № 6. P. 300. https://doi.org/10.3103/S0735272721060030
- Tohyama M. // Acoustic Signals and Hearing. Acad. Press, 2020. P. 89. https://doi.org/10.1016/B978-0-12-816391-7.00013-9
- Савченко А. В., Савченко В. В. // Измерит. техника. 2022. № 6. С. 60. https://doi.org/10.32446/0368-1025it.2022-6-60-66
- Palaparthi A., Titze I.R. // Speech Commun. 2020. V. 123. P. 98. https://doi.org/10.1016/j.specom.2020.07.003
- Ding J., Tarokh V., Yang Y. // IEEE Trans. 2018. V. IT-64. № 6. P. 4024. https://doi.org/10.1109/TIT.2017.2717599
- Min S.Y., Kim Y.K. // J. Korea Academia-industrial Cooperation Society. 2010. № 11. P. 3558. https://doi.org/10.5762/KAIS.2010.11.9.3558
- Савченко В.В. // Научные ведомости Белгород. ГУ. Сер. Экономика. Информатика. 2015. № 7. Вып. 34/1. С. 84.
- Sharma G., Umapathy K., Krishnan S. // Appl. Acoustics. 2020. V. 158. P. 107020. https://doi.org/10.1016/j.apacoust.2019.107020
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