Speech Audio Analytics
- Hamsa, S., Shahin, I., Iraqi, Y., Damiani, E., Nassif, A.B. and Werghi, N., 2023. Speaker identification from emotional and noisy speech using learned voice segregation and speech VGG. Expert Systems with Applications, 224, p.119871 [pdf] [demo]
- Hamsa, S., Shahin, I., Iraqi, Y. and Werghi, N., 2020. Emotion recognition from speech using wavelet packet transform cochlear filter bank and random forest classifier. IEEE Access, 8, pp.96994-97006. [pdf] [demo]
- Hamsa, S., Iraqi, Y., Shahin, I. and Werghi, N., 2021. An enhanced emotion recognition algorithm using pitch correlogram, deep sparse matrix representation and random forest classifier. IEEE Access, 9, pp.87995-88010. [pdf] [demo]
- Hamsa, S., Iraqi, Y., Shahin, I. and Werghi, N., 2021. Dominant voiced speech segregation and noise reduction pre-processing module for hearing aids and speech processing applications. In Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) 12 (pp. 395-403). Springer International Publishing.[pdf] [demo]
- Koya, S.H., Shahin, I., Iraqi, Y., Damiani, E. and Werghi, N., 2022, November. EA-VGG: A new approach for emotional speech classification. In 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (pp. 1-5). [pdf] [demo]
- Nassif, A.B., Shahin, I., Bader, M., Hassan, A. and Werghi, N., 2022. COVID-19 detection systems using deep-learning algorithms based on speech and image data. Mathematics, 10(4), p.564. [pdf] [demo]