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, D'autres exemples de talking box sont à écouter sur

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K. West and S. Et-cox, Features and classiers for the automatic classication of musical audio signals, Proceedings of the 5th International Society for Music Information Retrieval Conference, p.16, 2004.

B. Whitman and D. P. Et-ellis, Automatic record reviews, Proceedings of the 5th International Conference on Music Information Retrieval, p.470477, 2004.

G. A. Wiggins, Semantic gap ? ? Schemantic schmap ! ! Methodological considerations in the scientic study of music, Proceedings of the 11th IEEE International Symposium on Multimedia, p.477482, 2009.

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D. Wu, N. Sharma, and M. Et-blumenstein, Recent advances in videobased human action recognition using deep learning: A review, Proceedings of the 30th International Joint Conference on Neural Networks, p.28652872, 2017.

X. Wu, V. Kumar, R. Quinlan, J. Ghosh, J. Yang et al.,

D. J. Hand and D. Steinberg, Top 10 algorithms in data mining, Knowledge and Information Systems, vol.14, issue.1, p.137, 2008.

Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi et al.,

J. Dean, Google's neural machine translation system: Bridging the gap between human and machine translation, 2016.

Q. Yang and X. Et-wu, 10 challenging problems in data mining research, International Journal of Information Technology and Decision Making, vol.5, issue.4, p.597604, 2006.

A. Ycart and E. Et-benetos, A study on LSTM networks for polyphonic music sequence modelling, Proceedings of the 18th International Conference on Music Information Retrieval, p.421427, 2017.

D. Yin, S. D. Bond, and H. Et-zhang, Are bad reviews always stronger than good ? Asymmetric negativity bias in the formation of online consumer trust, Proceedings of 31st International Conference of Information Systems, p.118, 2010.

P. Zhang, X. Zheng, W. Zhang, S. Li, S. Qian et al., A deep neural network for modeling music, Proceedings of the 5th Annual ACM International Conference on Multimedia Retrieval, 2015.

T. Zhang and C. J. Et-kuo, Content-Based Audio Classication and Retrieval for Audiovisual Data Parsing, 2013.
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Y. C. Zhang, D. Ó. Séaghdha, D. Quercia, and T. Et-jambor, Auralist: Introducing serendipity into music recommendation, Proceedings of the 5th ACM International Conference on Web Search and Data Mining, p.1322, 2012.

P. Zhao, R. Jin, T. Yang, and S. C. Et-hoi, Online AUC maximization, 2011.

, Dans Proceedings of the 28th International Conference on Machine Learning, p.233240

, Glossaire Cent Échelle de représentation des notes musicales qui découpe un demi-ton en cent unités, vol.66, p.81

, Chanson Morceau comprenant au moins une piste audio sur laquelle ont été enregistrés des sons provenant directement ou indirectement de la voix humaine, p.185, 2934.

, Colliculus inférieur Élément du cerveau qui fait partie de la voie auditive ascendante entre l'oreille et le cortex auditif, vol.61, p.82, 1969.

, Échantillon Représentation numérique de la valeur de la pression acoustique à un instant donné au niveau du micro d'enregistrement, vol.24, p.176

, Émotion Réaction aective transitoire d'assez grande intensité, habituellement provoquée par une stimulation venue de l'environnement 1, vol.140, p.176

, Enregistrement Morceau enregistré sur support matérialisé ou dématérialisé xant une interprétation et composé d'un ensemble d'échantillons. 2224, 31, vol.38, p.176

, Harmonique Son dont la fréquence est un multiple entier d'une fréquence fondamentale propre à un autre son, p.62

, Heuristique Se dit d'une méthode de calcul qui fournit relativement rapidement, en temps polynomial, une solution réalisable mais pas nécessairement optimale pour un problème d'optimisation NP-dicile, vol.50, p.51

, Horse Se dit d'une méthode qui ne traite pas le problème qu'elle prétend résoudre, vol.100, p.138

, Instrumental Morceau ne comprenant aucune piste audio sur laquelle ont été enregistrés des sons provenant directement ou indirectement de la voix humaine, vol.182, p.185, 2934.

, Glossary Morceau ×uvre musicale interprétée, vol.143, p.181186, 1849.

, Mégadonnées Les mégadonnées désignent des ensembles de données croissants devenus si volumineux et complexes qu'ils dépassent l'intuition et les capacités humaines d'analyse ainsi que celles des outils informatiques classiques de gestion de bases de données ou de l'information. L'utilisation de ce mot est recommandée par la délégation générale à la langue française et aux langues de France 1. L'équivalent anglais des mégadonnées est big data

, Piste Un enregistrement comprend une ou plusieurs pistes qui correspondent à chaque source unique de sons, vol.30, p.176

, Reprise Se dit d'un morceau existant qui est rejoué, de façon similaire ou non, par un interprète diérent de celui de la version originale, vol.21, p.184

, Reproduire Capacité déterministe à générer à nouveau les résultats d'une expérience scientique, à ne pas confondre avec la réplicabilité [Drummond, p.176, 2009.

, Réplicabilité Fait de parvenir aux mêmes conclusions qu'une expérience scientique, vol.22, p.176

, Sentiment État aectif complexe et durable lié à certaines émotions ou représentations 2. 5, vol.25, p.26

, Streaming (anglicisme) Diusion ou transfert de données en ux continu, vol.48, p.93

, Sérendipité Capacité, art de faire une découverte, notamment scientique, par hasard. La sérendipité dénote également la découverte ainsi faite, vol.8, p.9

, Tessiture Ensemble de notes pouvant être émises avec homogénéité par un instrument

, Timbre Ensemble des caractéristiques sonores qui permettent d'identier un instrument

, Trame Subdivision arbitraire d'un enregistrement regroupant plusieurs échantillons audio, vol.26, p.185, 113116.

A. Acronymes and . Adaptive, SYNthetic sampling approach for imbalanced learning, p.51

, AllKNN All K-Nearest Neighbours, vol.49, p.53

, API Application Programming Interface, vol.32, p.136

, AUC Area Under the Curve, vol.16, p.185

, BPM Battements Par Minute. 63 bSMOTE borderline Synthetic Minority Over-sampling TEchnique, vol.50, p.53

, CIFAR Canadian Institute For Advanced Research, vol.19, p.20

, CNN Convolutional Neural Networks, vol.119, p.129

, Condensed-NN Condensed Nearest Neighbours. 49, 50, 53 CSV Comma Separated Value, p.33

, DAMP Digital Archive of Mobile Performances, p.39

, EFF Electronic Frontier Foundation. 33 ENN Edited Nearest Neighbours, vol.4951, p.53

, FMA Free Music Archive, vol.24, p.131

. Ga-algorithme-de-ghosal, , vol.108, p.185, 2013.

, GRU Gated Recurrent Unit, vol.120

, IFPI International Federation of the Phonographic Industry, vol.4, p.183

, IHT Instance Hardness Threshold, vol.50, p.53

, INRIA Institut National de Recherche en Informatique et en Automatique, p.30

, IRISA Institut de Recherche en Informatique et Systèmes Aléatoires. 30 ISO International Organization for Standardization. 32 ISRC International Standard Recording Code, vol.3133, p.183

, Acronyms k-NN K-Nearest Neighbours, p.103

, Kara1K Karaoke database of 1,000 tracks, vol.4044, p.183

L. Long,

. Mbid-musicbrainz-identier, , vol.32, p.33

, MDI Modulation Discrimination Interference, vol.62, p.76

, MFCC Mel-Frequency Cepstral Coecients, vol.85, p.128

, MIREX Music Information Retrieval Evaluation eXchange, vol.38, p.45

, MLP Multi Layer Perceptron, vol.118, p.185

, MP3 Moving Picture Experts Group Phase 1 Audio Layer III. 58 MSD Million Song Dataset, p.30

, NCR Neighboorhood Cleaning Rule, vol.50

, NearMiss NearMiss using nearest neighbours, vol.50, p.53

, OSS One-Sided Selection, vol.50, p.53

, PEA ProbaH2-10, ETRAll et AdaBoost, vol.109115, p.185

, RAM Random-Access Memory, vol.125127, p.129

, RANSAC RANdom Sample And Consensus, vol.103, p.115

, ReLU Rectied Linear Unit, vol.120, p.128

, RENN Repeated Edited Nearest Neighbours, vol.49, p.53

, RNN Recurrent Neural

, ROS Random minority Over-Sampling, vol.51, p.53

, RUS Random majority Under-Sampling, vol.50, p.53

, SATIN Set of Audio Tags and Identiers Normalized, vol.31, p.185, 128130.

. Shs-second-hand and . Songs, , p.38

, SMOTE Synthetic Minority Over-sampling TEchnique, vol.50, p.53

, SMOTEENN Synthetic Minority Over-sampling TEchnique followed by Edited Nearest Neighbours, vol.51, p.53

, SMOTETOMEK Synthetic Minority Over-sampling TEchnique followed by Tomek links, vol.51, p.53

, Acronyms SOFT1 rst Set Of FeaTures. 33, 34, 128 SPL Sound Pressure Level, vol.60, p.70

. Svm-support-vector-machine, , vol.51, p.115

, SVMBFF Support Vector Machine applied to Bags of Frames of Features, vol.108, p.185, 110113.

, SVMSMOTE Support Vector Machine and Synthetic Minority Over-sampling TEchnique, vol.51, p.53

, Tomek Extraction of majority-minority Tomek links, vol.50, p.53

, VQMM Vector Quantization and Markov Model. 108, 110113, vol.115, p.185

L. Des-figures,

. Mcfee, et des premières secondes du morceau Elysium d'Al Di Meola et (b) d'une image qui est une photographie personnelle, Exemples (a) d'un spectrogramme réalisé à partir du logiciel librosa, 2015.

. , Détails de l'ensemble des étapes de la chaîne de traitement de morceaux permettant de constituer des listes de lecture musicale