A Survey of Tensor Factorization Frameworks on Audio Modelling

Year 2015, Volume: 3 Issue: 1, 10 - 13, 17.01.2015

Unsal Gokdag

https://doi.org/10.18100/ijamec.70262

Abstract

This survey is about Tensor Factorization methods for audio modeling, which focuses on probabilistic latent tensor factorization and generalized coupled tensor factorization by expectation maximization method while using several linear and nonlinear distance measure methods

Keywords

Tensor Factorization, Probabilistic Latent Tensor Factorization

Original Research Paper

Abstract

References

• Y. K. Yılmaz and A. T. Cemgil, “Algorithms for probabilistic latent tensor factorization”, Signal Processing, vol. 92, no. 8, pp. 1853 – 1863, 2011.
• T. Cemgil, U. Simsekli, and Y. C. Subakan, “Probabilistic latent tensor factorization framework for audio modeling,” in Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics WASPAA ’11, 2011, pp. 137–140.
• U. Simsekli, A. T. Cemgil, and Y. K. Yilmaz, “Score guided audio restoration via generalised coupled tensor factorisation,” in ICASSP, 2012, accepted.
• S. Kullback and R. A. Leibler, “On information and sufficiency,” Ann. Math. Statistics, vol. 22, pp. 79–86, 1951
• S. Saito and F. Itakura, “Frequency spectrum deviation between speakers,” Speech Communication, vol. 2, no. 2-3, pp. 149–152, 1983.
• K. Yilmaz and A. T. Cemgil, “Probabilistic latent tensor factorisation,” in Proc. of International Conference on Latent Variable analysis and Signal Separation, vol. 6365, 2010, pp. 346–353.
• L. Xiong, X. Chen, T.-K. Huang, J. Schneider, and J. G. Carbonell, “Temporal collaborative filtering with bayesian probabilistic tensor factorization,” in Proceedings of SIAM Data Mining, 2010.
• P. Smaragdis and J. Brown, “Non-negative matrix factorization for polyphonic music transcription,” in Applications of Signal Processing to Audio and Acoustics, 2003 IEEE Workshop on., oct. 2003, pp. 177 – 180.
• P. Smaragdis, “Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs,” in Independent Component Analysis and Blind Signal Separation, ser. Lecture Notes in Computer Science, C. Puntonet and A. Prieto, Eds. Springer Berlin Heidelberg, 2004, vol. 3195, pp. 494 – 499.
• M. N. Schmidt and M. Mørup, “Nonnegative matrix factor 2-d deconvolution for blind single channel source separation,” in Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation, ser. ICA’06. Berlin, Heidelberg: SpringerVerlag, 2006, pp. 700–707.
• Klapuri, T. Virtanen, and T. Heittola, “Sound source separation in monaural music signals using excitation-filter model and em algorithm,” in Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, march 2010, pp. 5510 –5513.Allahverdi N. Some Applications of Fuzzy Logic in Medical Area, Proceedings on the 3rd International Conference on Application of Information and Communication Technologies (AICT2009), Published by IEEE, 14-16 October 2009, Azerbaijan, Baku