Support vector-based online detection of abrupt changes

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

Volumn 4, Issue , 2003, Pages 872-875

  Desobry, Frédéric ^a   Davy, Manuel ^a  

^a INSTITUT DE RECHERCHE EN COMMUNICATIONS ET CYBERNÉTIQUE DE NANTES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DETECTORS; FOURIER TRANSFORMS; PROBABILITY DENSITY FUNCTION; SET THEORY;

ABRUPT CHANGES;

LEARNING SYSTEMS;

EID: 0141744658     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. M. Basseville and I. Nikiforov, Detection of Abrupt Changes - Theory and Application, Prentice-Hall, April 1993.
2. A. Smola and B. Schölkopf, Learning with Kernels, MIT press, 2002.
3. B. Schölkopf, J.C. Platt, J. Shawe-Taylor, A.J. Smola, and R.C. Williamson, "Estimating the support of a high-dimensional distribution," Neural Computation, vol. 13, no. 7, pp. 1443-1471, 2001.
4. V. Vapnik, The Nature of Statistical Learning Theory, Springer, N.Y., 1995.
5. A. Gretton and F. Desobry, "On-line one-class ν-support vector machines, an application to signal segmentation," in IEEE ICASSP, Hong-Kong, China, April 2003 (submitted).
6. M. Davy and S. Godsill, "Detection of abrupt spectral changes using support vector machines, an application to audio signal segmentation," in IEEE ICASSP-02, Orlando, USA, May 2002.
7. H. Laurent and C. Doncarli, "Stationarity index for abrupt changes detection in the time-frequency plane," IEEE Signal Processing Letters, vol. 5, no. 2, pp. 43 - 45, February 1998.