RAQAMLI SIGNALLARGA VAQT SOHASIDA ISHLOV BERISH
DOI:
https://doi.org/10.5281/zenodo.17855191Keywords:
Zero-Crossing Rate, raqamli signal, chastota sohasi, vaqt sohasi, Fourier transformatsiyasi.Abstract
Ushbu maqolada raqamli signallarga vaqt sohasida ishlov berishning asosiy nazariy va amaliy tamoyillari yoritiladi. Vaqt va chastota sohasidagi tahlil usullari, Zero-Crossing Rate (ZCR), Fourier transformatsiyasi, avtoregressiya tahlili, konvolyutsiya, filtrlash hamda korrelyatsiya kabi fundamental DSP (Digital Signal Processing) operatsiyalarining qo‘llanilishi ko‘rib chiqiladi.
References
Arifin, C., & Junaedi, H. (2018). Emotion Sound Classification with Support Vector Machine Algorithm. Kinetik, 3(2). https://doi.org/10.22219/kinetik.v3i2.610
Z. Hosseini, S. M. Ahadi and N. Faraji, "Speech emotion classification via a modified Gaussian Mixture Model approach," 7'th International Symposium on Telecommunications (IST'2014), Tehran, Iran, 2014, pp. 487-491, doi: 10.1109/ISTEL.2014.7000752.
Ainurrochman, I. I. Febriansyah and U. L. Yuhana, "SER: Speech Emotion Recognition Application Based on Extreme Learning Machine," 2021 13th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia, 2021, pp. 179-183, doi: 10.1109/ICTS52701.2021.9609016.
Fei Wang, Shichao Wu, Weiwei Zhang, Zongfeng Xu, Yahui Zhang, Chengdong Wu, Sonya Coleman, Emotion recognition with convolutional neural network and EEG-based EFDMs,Neuropsychologia,Volume 146,2020,107506,ISSN0028-3932, https://doi.org/10.1016/j.neuropsychologia.2020.107506.
J. J. Zhao and K. Cho, "Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples," in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 11555-11563, doi: 10.1109/CVPR.2019.01183.