Qualitative assessment of intrinsic mode functions of empirical mode decomposition

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2008, Pages 1905-1908

  Chen, Mo ^a   Mandic, Danilo P ^a   Kidmose, Preben ^b   Ungstrup, Michael ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b Widex A/S   (Denmark)

Author keywords

Delay vector variance (DVV); Empirical mode decomposition (EMD); Intrinsic mode function (IMF); Non stationary; Nonlinear

Indexed keywords

ACOUSTICS; PHASE SPACE METHODS; SIGNAL ANALYSIS; SIGNAL PROCESSING; SINGULAR VALUE DECOMPOSITION; SPEECH; STRUCTURES (BUILT OBJECTS);

DELAY VECTOR VARIANCE (DVV); EMPIRICAL MODE DECOMPOSITION (EMD); INTRINSIC MODE FUNCTION (IMF); NON-STATIONARY; NONLINEAR;

DATA COMPRESSION;

EID: 51449114846     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2008.4518007     Document Type: Conference Paper

References (9)

1. L. Stankovic and V. N. Ivanovic, "Further results on the minimum variance time-frequency distribution kernels," IEEE Transactions on Signal Processing, vol. 45, no. 6, pp. 1650-1656, 1997.
2. L. Stankovic, "Time-frequency distributions with complex argument," IEEE Transactions on Signal Processing, vol. 50, no. 3, pp. 475-486, 2002.
3. N. E. Huang, Z Shen, S. R. Long, M. C. Wu, H. H. Shin, Q. Zheng, N. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis," Proc. R. Soc. Lond. A., 1998.
4. T. Schreiber, "Interdisciplinary application of nonlinear time series methods," Phys. Rep., vol. 308, no. 1, pp. 1-64, 1999.
5. T. Gautama, D.P. Mandic, and M.M. Van Hulle, "The delay vector variance method for detecting determinism and nonlinearity in time series," Physica D, vol. 190, no. 3-4, pp. 167-176, 2004.
6. N. E. Huang and S.S.P. Shen, "Introduction to the hilbert-huang transform and its related mathematical problems, hilbert-huang transform and its application," Interdisciplinary Mathematical Science, World Scientific, no. 1, 2005.
7. L. Cao, "Practical method for determining the minimum embedding dimension of a scalar time series," Physica D, vol. 110, pp. 43-50, 1997.
8. D.P. Mandic and J.A. Chambers, Recurrent Neural Networks for Prediction: Learning Algorithms Architecture and Stability, Wiley: Chichester, 2001.
9. M. Henon, "A two-dimensional mapping with a strange attractor," Commun. Math. Phys., vol. 50, pp. 69-77, 1976.