Musab T. S. Al-Kaltakchi

Orcid: 0000-0001-5542-9144

According to our database1, Musab T. S. Al-Kaltakchi authored at least 12 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Study on Sensing Urine Concentrations in Water Using a Microwave Sensor Based on Hilbert Structure.
Sensors, June, 2024

2023
Ensemble System of Deep Neural Networks for Single-Channel Audio Separation.
Inf., 2023

2021
Combined i-Vector and Extreme Learning Machine Approach for Robust Speaker Identification and Evaluation with SITW 2016, NIST 2008, TIMIT Databases.
Circuits Syst. Signal Process., 2021

2020
Comparisons of extreme learning machine and backpropagation-based i-vector approach for speaker identification.
Turkish J. Electr. Eng. Comput. Sci., 2020

2019
Thorough evaluation of TIMIT database speaker identification performance under noise with and without the G.712 type handset.
Int. J. Speech Technol., 2019

2018
Personal verification based on multi-spectral finger texture lighting images.
IET Signal Process., 2018

2017
Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects.
EURASIP J. Adv. Signal Process., 2017

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion.
Digit. Signal Process., 2017

Speaker identification evaluation based on the speech biometric and i-vector model using the TIMIT and NTIMIT databases.
Proceedings of the 5th International Workshop on Biometrics and Forensics, 2017

Comparison of I-vector and GMM-UBM approaches to speaker identification with TIMIT and NIST 2008 databases in challenging environments.
Proceedings of the 25th European Signal Processing Conference, 2017

2016
Study of statistical robust closed set speaker identification with feature and score-based fusion.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2016

Study of fusion strategies and exploiting the combination of MFCC and PNCC features for robust biometric speaker identification.
Proceedings of the 4th International Conference on Biometrics and Forensics, 2016


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