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NUTQ SIGNALIDAN SHOVQINLARNI KETMA-KET BARTARAF ETISH ALGORITMI

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INNOVATION TECHNOLOGIES

INNOVATION TECHNOLOGIES

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ARTICLE ANNOTATION

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Annotatsiya. Nutq signalini shovqindan tozalash muammosi nutq tanish tizimlari, mobil aloqa qurilmalari va ovozli interfeyslar uchun dolzarb vazifa hisoblanadi. Mazkur tadqiqotda Self Organizing Map (SOM) va Spectral Subtraction usullarini birlashtirgan yangi yondashuv taklif etilgan. Bu yondashuv shovqinli klasterlarni aniqlashda SOM neyron tarmog‘idan foydalanib, energiya va chastota xususiyatlarini kombinatsiya qilish orqali nutqni samarali tozalaydi. Tadqiqot jarayonida shovqin taxmini uchun Minimum Statistics Noise Estimation usuli, xususiyatlarni adaptiv tanlash strategiyasi qo‘llanilgan va shovqinni turli darajalarida (1% dan 25% gacha) tajribalar o‘tkazilgan. PESQ metrikasi yordamida baholangan natijalar taklif etilgan yondashuv an’anaviy Veyvlet va Spectral Subtraction kabi usullarga nisbatan yuqori samaradorlikka egaligini ko‘rsatgan. Ushbu yondashuvni afzalligi shovqinli klasterlarni aniqlashni takomillashtirilgan algoritmi va nutq tabiiyligini saqlab qolish uchun optimallashtirilgan post-processing bosqichida namoyon bo‘lgan. Tadqiqot natijalari ko‘rsatishicha, SOM va Spectral Subtraction usullari kombinatsiyasi shovqinli muhitda nutq signallarini tozalashda samarali yechim hisoblanadi.

AUTHORS

N.Niyozmatova

“Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti huzuridagi Fundamental va amaliy tadqiqotlar instituti,

Y.Yuldashev

Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti,

Q.Jalelov

”Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti,

A.Samijonov

”Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti,

B.Abdullaeva

Namangan davlat universiteti

Tags

# Kalit so‘zlar: Self-Organizing M# Spectral Subtraction# Mel-Frequency Cepstral Coeffici# Log-Mel spektrogrammasi

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Article Identifiers

ROI:

https://eroi.uz/11.0032/A1125-0095

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Not Available

References

Ephraim, Y., & Malah, D. (1984). Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech, and Signal Processing, 32(6), 1109-1121.

Mamatov, N., Niyozmatova, N., & Samijonov, A. (2021). Software for preprocessing voice signals. International Journal of Applied Science and Engineering, 18(1), 1-8.

Niyozmatova, N. N., Jalelov, N. K., Samijonov, N. B., & Madrahimova, N. M. (2024). Eliminating noise from a speech signal based on a pair of filters. International Journal of Science and Research Archive, 13(2), 401–410. https://doi.org/10.30574/ijsra.2024.13.2.2058

Boll, S.F. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(2), 113-120.

Lim, J.S., & Oppenheim, A.V. (1979). Enhancement and bandwidth compression of noisy speech. Proceedings of the IEEE, 67(12), 1586-1604.

Ramirez, J., Górriz, J.M., & Segura, J.C. (2007). Voice activity detection: fundamentals and speech recognition system robustness. In M. Grimm & K. Kroschel (Eds.), Robust Speech Recognition and Understanding (pp. 1-22). I-Tech Education and Publishing.

Martin, R. (2001). Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE Transactions on Speech and Audio Processing, 9(5), 504-512.

Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59-69.

Zhang, X.L., & Wu, J. (2013). Deep belief networks based voice activity detection. IEEE Transactions on Audio, Speech, and Language Processing, 21(4), 697-710.

Ramírez, J., Segura, J.C., Benítez, C., De La Torre, A., & Rubio, A. (2004). Efficient voice activity detection algorithms using long-term speech information. Speech Communication, 42(3-4), 271-287.