Conception des modÃ¨les d'observation audio temps rÃ©el Ã l ... - atiam

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[13] Isabelle Guyon, Steve Gunn, Asa Ben Hur, and Gideon Dror. Resultanalysis of the nips 2003 feature selection challenge. 2004.[14] M. A. Hall. Correlation-based Feature Subset Selection for Machine Learning.PhD thesis, University of Waikato, Hamilton, New Zealand, 1998.[15] Jin Huang and Charles X. Ling. Using auc and accuracy in evaluatinglearning algorithms, 2003.[16] George H. John and Pat Langley. Estimating continuous distributionsin bayesian classifiers. In Eleventh Conference on Uncertainty in ArtificialIntelligence, pages 338–345, San Mateo, 1995. Morgan Kaufmann.[17] Kenji Kira and Larry A. Rendell. A practical approach to feature selection.In ML92 : Proceedings of the ninth international workshop on Machinelearning, pages 249–256, San Francisco, CA, USA, 1992. Morgan KaufmannPublishers Inc.[18] Ron Kohavi and George H. John. Wrappers for feature subset selection.Artif. Intell., 97(1-2) :273–324, 1997.[19] Igor Kononenko. Estimating attributes : Analysis and extensions of RE-LIEF. In European Conference on Machine Learning, pages 171–182, 1994.[20] P. Leveau, L. Daudet, and G. Richard. Methodology and tools for theevaluation of automatic onset detection algorithms in music. In In Proc.ISMIR 2004, Barcelona, 2004.[21] Andrew W. Moore and Mary S. Lee. Efficient algorithms for minimizingcross validation error. In International Conference on Machine Learning,pages 190–198, 1994.[22] Pavel Paclik, Robert P.W. Duin, Geert M.P. van Kempen, and ReinhardKohlus. On feature selection with measurement cost and grouped features,2002.[23] Geoffroy Peeters. A large set of audio features for sound description(similarity and classification) in the cuidado project. Technical report,2004. CUIDADO I.S.T. Project Report.[24] Lawrence R. Rabiner. A tutorial on hidden markov models and selectedapplications in speech recognition. pages 267–296, 1990.[25] C. Raphael. Automatic segmentation of acoustic musical signals usinghidden markov models. In IEEE Trans. Pattern Analysis and Machine Intelligence,page 21(4), 1999.[26] Marko Robnik-Sikonja and Igor Kononenko. Theoretical and empiricalanalysis of relieff and rrelieff. Mach. Learn., 53(1-2) :23–69, 2003.[27] Diemo Schwarz, Nicola Orio, and Norbert Schnell. Robust polyphonicmidi score following with hidden markov models. In Proceedings ofthe International Computer Music Conference (ICMC), November 1-6 2004.Miami, Florida.[28] Michèle Sebag. Sélection d’attributs, 2005. Transparents de cours.[29] Matthew G. Smith and Larry Bull. Feature construction and selectionusing genetic programming and a genetic algorithm, 2003. EuroGP 2003,LNCS 2610.
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Université Pierre & Marie Curie, P
Page 5:
AbstractMusical interaction between
Page 8 and 9:
viii
Page 10 and 11: xTABLE DES MATIÈRES5 Discussion et
Page 12 and 13: stage. Celles-ci mettent en exergue
Page 14 and 15: 4 Pré-requisindépendamment l’un
Page 16 and 17: 6 Pré-requisFenêtre de signalCalc
Page 18 and 19: 8 Pré-requissont calculés à part
Page 20 and 21: 10 Sélection de descripteursà la
Page 22 and 23: 12 Sélection de descripteursSi les
Page 24 and 25: 14 Sélection de descripteursclasse
Page 26 and 27: 16 Sélection de descripteurset l
Page 28 and 29: 18 Sélection de descripteursFIG. 2
Page 30 and 31: 20 Sélection de descripteurs et ca
Page 38 and 39: 28 Expérimentationsune représenta
Page 40 and 41: 30 Expérimentations4.4 Première a
Page 42 and 43: 32 Expérimentationsvaleurs mesuré
Page 44 and 45: 34 Expérimentations181716Wrapper (
Page 46 and 47: 36 ExpérimentationsGain de perform
Page 48 and 49: 38 ExpérimentationsCoût de calcul
Page 50 and 51: 40 Expérimentations0.850.8Aire sou
Page 52 and 53: 42 Discussion et directions futures
Page 54 and 55: 44 Discussion et directions futures
Page 57: ConclusionDurant ce stage, nous avo
Page 63 and 64: Annexes
Page 65: Nom du descripteurNombre de dimensi
Page 69: Liste des tableaux4.1 Erreur d’ap
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