Технологии инженерных и информационных систем. 2016; : 52-58
Анализ методов слепого разделения сигнала для решения задачи разделения аудио источников
Аннотация
Список литературы
1. Parathai P. Blind source separation using statistical nonnegative matrix factorization: diss. – Newcastle University, 2015.
2. Barchiesi D. Sparse approximation and dictionary learning with applications to audio signals: diss. Queen Mary University of London, 2013.
3. Zibulevsky M., Pearlmutter B.A. Blind source separation by sparse decomposition. AeroSense 2000. International Society for Optics and Photonics, 2000. P. 165-174.
4. Gao B. Single channel blind source separation: diss. University of Newcastle Upon Tyne, 2011.
5. Mirzal A. NMF versus ICA for blind source separation. Advances in Data Analysis and Classification. 2014. P. 1-24.
6. Behera S.K. Fast ICA for blind source separation and its implementation: diss. National Institute of Technology Rourkela, 2009.
7. Hyvärinen A., Karhunen J., Oja E. Independent component analysis. John Wiley & Sons, 2004. P. 480.
8. Babaie-Zadeh M., Bahmani B., Jutten C. ICA by mutual information minimization: An approach for avoiding local minima. 2005 13th European Signal Processing Conference. IEEE, 2005. P. 1-4.
9. MacKay D.J.C. Maximum likelihood and covariant algorithms for independent component analysis. Technical Report Draft 3.7. Cavendish Laboratory, University of Cambridge, Madingley Road, Cambridge CB3 0HE, 1996.
10. Lee D.D., Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature. 1999. Vol. 401. No. 6755. P. 788-791.
11. Lee D.D., Seung H.S. Algorithms for non-negative matrix factorization. Advances in neural information processing systems. 2001. P. 556-562.
12. Rosca J. et al. (ed.). Independent Component Analysis and Blind Signal Separation. 6th International Conference, ICA. 2006, Charleston, SC, USA, March 5-8, 2006, Proceedings. Springer, 2006. Vol. 3889.
Title in english. 2016; : 52-58
Анализ методов слепого разделения сигнала для решения задачи разделения аудио источников
Abstract
References
1. Parathai P. Blind source separation using statistical nonnegative matrix factorization: diss. – Newcastle University, 2015.
2. Barchiesi D. Sparse approximation and dictionary learning with applications to audio signals: diss. Queen Mary University of London, 2013.
3. Zibulevsky M., Pearlmutter B.A. Blind source separation by sparse decomposition. AeroSense 2000. International Society for Optics and Photonics, 2000. P. 165-174.
4. Gao B. Single channel blind source separation: diss. University of Newcastle Upon Tyne, 2011.
5. Mirzal A. NMF versus ICA for blind source separation. Advances in Data Analysis and Classification. 2014. P. 1-24.
6. Behera S.K. Fast ICA for blind source separation and its implementation: diss. National Institute of Technology Rourkela, 2009.
7. Hyvärinen A., Karhunen J., Oja E. Independent component analysis. John Wiley & Sons, 2004. P. 480.
8. Babaie-Zadeh M., Bahmani B., Jutten C. ICA by mutual information minimization: An approach for avoiding local minima. 2005 13th European Signal Processing Conference. IEEE, 2005. P. 1-4.
9. MacKay D.J.C. Maximum likelihood and covariant algorithms for independent component analysis. Technical Report Draft 3.7. Cavendish Laboratory, University of Cambridge, Madingley Road, Cambridge CB3 0HE, 1996.
10. Lee D.D., Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature. 1999. Vol. 401. No. 6755. P. 788-791.
11. Lee D.D., Seung H.S. Algorithms for non-negative matrix factorization. Advances in neural information processing systems. 2001. P. 556-562.
12. Rosca J. et al. (ed.). Independent Component Analysis and Blind Signal Separation. 6th International Conference, ICA. 2006, Charleston, SC, USA, March 5-8, 2006, Proceedings. Springer, 2006. Vol. 3889.
