S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, 1993.

F. Bimbot, J. Bonastre, C. Fredouille, G. Gravier, I. Magrin-chagnolleau et al., A Tutorial on Text-Independent Speaker Verification, EURASIP Journal on Advances in Signal Processing, vol.2004, issue.4, pp.430-451, 2004.
DOI : 10.1155/S1110865704310024

URL : https://hal.archives-ouvertes.fr/hal-01434501

L. R. Rabiner and R. W. Schäfer, Introduction to digital signal processing, Found. Trends Inform. Retrieval, vol.1, pp.1-2, 2007.

M. Basseville and N. Nikiforov, The Detection of Abrupt Changes (Information and System Sciences Series), 1993.

P. Fearnhead, Exact and efficient Bayesian inference for multiple changepoint problems, Statistics and Computing, vol.12, issue.2, pp.203-213, 2006.
DOI : 10.1007/s11222-006-8450-8

J. Chen and A. K. Gupta, Parametric Statistical Change-Point Analysis, 2000.
DOI : 10.1007/978-0-8176-4801-5

E. Lehmann and J. Romano, Testing Statistical Hypotheses, 2005.

D. A. Forsyth and J. Ponce, Computer Vision: A Modern Approach, 2nd, 2012.

J. Shawe-taylor and N. Cristianini, Kernel Methods for Pattern Analysis
DOI : 10.1017/CBO9780511809682

B. Schölkopf and A. J. Smola, Learning with Kernels, 2002.

C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), 2005.

Z. Harchaoui, F. Bach, and E. Moulines, Testing for homogeneity with kernel Fisher discriminant analysis, Advances in Neural Information Processing Systems, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00270806

Z. Harchaoui, F. Bach, and E. Moulines, Kernel change-point analysis, Advances in Neural Information Processing Systems, 2009.

A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. J. Smola, A kernel two-sample test, J. Mach. Learn. Res, vol.13, pp.723-773, 2012.

M. Sugiyama, T. Suzuki, and T. Kanamori, Density Ratio Estimation in Machine Learning, 2012.
DOI : 10.1017/CBO9781139035613

A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Schölkopf et al., A kernel statistical test of independence, Advances in Neural Information Processing Systems, 2008.

Z. Harchaoui, F. Vallet, A. Lung-yut-fong, and O. Cappé, A regularized kernelbased approach to unsupervised audio segmentation, Proc. Int. Conf. Acoustics, Speech and Signal Processing, pp.1665-1668, 2009.

O. Gillet, S. Essid, and G. Richard, On the Correlation of Automatic Audio and Visual Segmentations of Music Videos, IEEE Transactions on Circuits and Systems for Video Technology, vol.17, issue.3, pp.347-355, 2007.
DOI : 10.1109/TCSVT.2007.890831

R. Brunelli, O. Mich, and C. M. Modena, A Survey on the Automatic Indexing of Video Data,, Journal of Visual Communication and Image Representation, vol.10, issue.2, pp.78-112, 1999.
DOI : 10.1006/jvci.1997.0404

A. F. Smeaton, P. Over, and A. R. Doherty, Video shot boundary detection: Seven years of TRECVid activity, Computer Vision and Image Understanding, vol.114, issue.4, pp.411-418, 2010.
DOI : 10.1016/j.cviu.2009.03.011

F. Vallet, Structuration automatique de shows télévisés, 2011.

D. A. Reynolds and P. Torres-carrasquillo, Approaches and Applications of Audio Diarization, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp.953-956, 2005.
DOI : 10.1109/ICASSP.2005.1416463

Z. Harchaoui and O. Cappé, Retrospective multiple change-point estimation with kernels, Proc. IEEE Workshop Statistical Signal Processing (SSP), pp.768-772, 2007.

H. Wendland, Scattered Data Approximation (Cambridge Monographs on Applied and Computational Mathematics), 2005.

F. Cucker and D. X. Zhou, Learning Theory: An Approximation Theory Viewpoint, 2007.
DOI : 10.1017/CBO9780511618796

M. Hein and O. Bousquet, Hilbertian metrics and positive-definite kernels on probability measures, Proc. AISTATS, pp.136-143, 2004.

K. Fukumizu, F. Bach, and A. Gretton, Statistical convergence of kernel canonical correlation analysis, J. Mach. Learn. Res, vol.8, issue.8, pp.361-383, 2007.

E. Lehmann, Elements of Large-Sample Theory, 1999.
DOI : 10.1007/b98855

T. W. Anderson, An Introduction to Multivariate Statistical Analysis, 2003.

L. Wasserman, All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics), 2004.
DOI : 10.1007/978-0-387-21736-9

B. Schölkopf, A. Smola, and K. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

L. Song, J. Huang, A. Smola, and K. Fukumizu, Hilbert space embeddings of conditional distributions with applications to dynamical systems, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.121, 2009.
DOI : 10.1145/1553374.1553497

N. N. Vakhania, V. I. Tarieladze, S. A. Chobanyan, F. R. Bach, and M. I. Jordan, Probability Distributions on Banach Spaces. Amsterdam, The Netherlands: Reidel Kernel independent component analysis, J. Mach. Learn. Res, vol.36, issue.3, pp.1-48, 1987.

C. A. Micchelli, Y. Xu, H. K. Zhang-]-b, A. Sriperumbudur, K. Gretton et al., Universal kernels Injective Hilbert space embeddings of probability measures, Proc. 21st Annu. Conf. Learning Theory (COLT), pp.2651-2667, 2006.

I. Steinwart and A. Christmann, Support Vector Machines, 2008.

G. Blanchard, O. Bousquet, L. Zwald41, ]. S. Mika, G. Raetsch et al., Statistical properties of kernel principal component analysis Constructing descriptive and discriminative non-linear features: Rayleigh coefficients in kernel feature spaces Testing for homogeneity with kernel Fisher discriminant analysis A kernel method for the two-sample problem A fast, consistent kernel two-sample test, Advances in Neural Information Processing Systems Advances in Neural Information Processing Systems, pp.259-294, 2003.

N. H. Anderson, P. Hall, and D. M. Titterington, Two-Sample Test Statistics for Measuring Discrepancies Between Two Multivariate Probability Density Functions Using Kernel-Based Density Estimates, Journal of Multivariate Analysis, vol.50, issue.1, pp.41-54, 1994.
DOI : 10.1006/jmva.1994.1033

D. Sejdinovic, A. Gretton, B. K. Sriperumbudur, K. Fukumizu, T. Kanamori et al., Hypothesis testing using pairwise distances and associated kernels f-divergence estimation and twosample homogeneity test under semiparametric density-ratio models Estimating divergence functionals and the likelihood ratio by convex risk minimization Statistical analysis of kernel-based least-squares density-ratio estimation An online support vector machine for abnormal events detection An online kernel change detection algorithm Estimation of minimum measure sets in reproducing kernel Hilbert spaces and applications, Proc. Int. Conf. Machine Learning (ICML). [53] M. Davy, F. Desobry, and S. Canu Proc. IEEE Int. Conf. Acoustics , Speech and Signal Processing, pp.708-720, 2005.

R. Vert, J. Vert, T. Jebara, R. Kondor, and A. Howard, Consistency and convergence rates of one-class SVM and related algorithms Probability product kernels, J. Mach. Learn. Res. J. Mach. Learn. Res, vol.7, issue.5, pp.817-854, 2004.

A. Gretton and L. Györfi, Consistent nonparametric tests of independence, Principe, W. Liu, and S. Haykin, Kernel Adaptive Filtering: A Comprehensive Introduction, pp.1391-1423, 2010.

]. K. Fukumizu, F. R. Bach, and A. Gretton, Statistical consistency of kernel canonical correlation analysis, Measuring statistical dependence with Hilbert?Schmidt norms Proc. 16th Int. Conf. Algorithmic Learning Theory (ALT), pp.361-383, 2005.

H. Shen, S. Jegelka, and A. Gretton, Fast Kernel-Based Independent Component Analysis, IEEE Transactions on Signal Processing, vol.57, issue.9, pp.3498-3511, 2009.
DOI : 10.1109/TSP.2009.2022857