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.
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.
| № | Муаллифнинг исми | Лавозими | Ташкилот номи |
|---|---|---|---|
| 1 | Niyozmatova N.A. | texnika fanlari bo‘yicha falsafa doktori (PhD), dotsent, | “Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti huzuridagi Fundamental va amaliy tadqiqotlar instituti, |
| 2 | Yuldashev .S. | texnika fanlari bo‘yicha falsafa doktori (PhD), dotsent | Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti, |
| 3 | Jalelov .M. | doktorant (PhD) | ”Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti, |
| 4 | Samijonov A.N. | talaba | ”Toshkent irrigatsiya va qishloq xo‘jaligini mexanizatsiyalash muhandislari instituti” Milliy tadqiqot universiteti, |
| 5 | Abdullaeva B.M. | doktorant (PhD) | Namangan davlat universiteti |
| № | Ҳавола номи |
|---|---|
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