Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection
Author:
Publisher
ASTES Journal
Subject
Management of Technology and Innovation,Physics and Astronomy (miscellaneous),Engineering (miscellaneous)
Link
https://www.astesj.com/publications/ASTESJ_060515.pdf
Reference42 articles.
1. A. Alghamdi, G. Healy, H. Abdelhafez "Real Time Blind Audio Source Separation Based on Machine Learning Algorithms", IEEE 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 35-40, Oct, 2020, doi:10.1109/NILES50944.2020.9257891
2. M. Pal, R. Roy, J. Basu, M. S. Bepari, "Blind Source Separation: A Review and Analysis," 2013 International Conference Oriental COCOSDA held jointly with 2013 Conference on Asian Spoken Language Research and Evaluation, 1-5, 2013, doi:10.1109/ICSDA.2013.6709849.
3. K. Ball, N. Bigdely-Shamlo, T. Mullen, K. Robbins, "PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG," Computational intelligence and neuroscience 2016(73), 1-20, 2016, doi:10.1155/2016/9754813.
4. E. M. Grais, H. Wierstorf , D. Wa, R. Mason and M. D. Plumbley, "Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks.," 27th European Signal Processing Conference (EUSIPCO). IEEE, 1-5, 2019.
5. N. Makishima, S. Mogami, N. Takamune, D. Kitamura, H. Sumino, S. Takamichi , H. Saruwatari and . N. Ono, "Independent Deeply Learned Matrix Analysis for Determined Audio Source Separation," EEE/ACM Transactions on Audio, Speech, and Language Processing, 27(10), 1601-1615, 2019.
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