Academic literature on the topic 'Détection de reprise'
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Journal articles on the topic "Détection de reprise":
Lakhdissi, H., B. Haddada, A. Lahlou Kassi, and M. Thibier. "Conduite de la reproduction en grands troupeaux laitiers dans les conditions marocaines. II. Reprise de l'activité cyclique <em>post-partum</em>." Revue d’élevage et de médecine vétérinaire des pays tropicaux 41, no. 4 (April 1, 1988): 441–47. http://dx.doi.org/10.19182/remvt.8670.
Lakhdissi, H., B. Haddada, A. Lahlou Kassi, and M. Thibier. "Conduite de la reproduction en grands troupeaux laitiers dans les conditions marocaines. III. Reprise des chaleurs et anoestrus post-insémination naturelle." Revue d’élevage et de médecine vétérinaire des pays tropicaux 42, no. 2 (February 1, 1989): 261–66. http://dx.doi.org/10.19182/remvt.8853.
Cisse, Mohamed Talla, Soussou Sambou, Yaya Dieme, Clément Diatta, and Mamadou Bop. "Analyse des écoulements dans le bassin du fleuve Sénégal de 1960 à 2008." Revue des sciences de l’eau 27, no. 2 (June 13, 2014): 167–87. http://dx.doi.org/10.7202/1025566ar.
Anadón-Irizarry, Verónica, Rafael González, Iván Llerandi-Román, and Marconi Campos-Cerqueira. "Status and recommendations for the recovery of the Elfin-woods Warbler (<em>Setophaga angelae</em>) in Puerto Rico." Journal of Caribbean Ornithology 30, no. 1 (December 16, 2017): 28–32. http://dx.doi.org/10.55431/jco.2017.30(1).28-32.
Ballarin, A., A. Jancys, P. Tinsy, Au Van Gossum, A. Bustillo, M. Arvanitaki, J. C. Preiser, and A. Van Gossum. "Détection et prise en charge de la dénutrition à l’admission des patients dans les unités de soins : analyse « point-prévalence » répétée à trois reprises." Nutrition Clinique et Métabolisme 31, no. 3 (September 2017): 256. http://dx.doi.org/10.1016/j.nupar.2017.06.005.
Lambert, Serge. "Compactage Horizontal Statique : retours d’expérience." Revue Française de Géotechnique, no. 162 (2020): 3. http://dx.doi.org/10.1051/geotech/2020005.
Titécat, M., S. Nguyen, H. Dezeque, M. Valette, E. Beltrand, N. Blondiaux, C. LoÏez, H. Migaud, and E. Senneville. "COL 6-02 - Détection rapide des staphylocoques résistants à la méticilline (SRM) au cours des reprises pour infections de prothèses ostéo-articulaires (IPOAS) : étude rétrospective à partir de 215 patients consécutifs." Médecine et Maladies Infectieuses 46, no. 4 (June 2016): 13. http://dx.doi.org/10.1016/s0399-077x(16)30282-7.
Grandjean, Dominique, Florine Hache, Capucine Gallet, Hélène Bacqué, Marc Blondot, Loïc Desquilbet, Holger Volk, et al. "Acceptation par le public de la détection olfactive canine de la CoViD-19 : à propos d’une enquête internationale avant redéploiement." Bulletin de l'Académie vétérinaire de France 176 (2023). http://dx.doi.org/10.3406/bavf.2023.71014.
Eschenbrenner, Céline. "The Sound of Difference." Social Anthropology/Anthropologie sociale, December 1, 2022, 1–15. http://dx.doi.org/10.3167/saas.2022.101702.
Dissertations / Theses on the topic "Détection de reprise":
Vaglio, Andrea. "Leveraging lyrics from audio for MIR." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT027.
Lyrics provide a lot of information about music since they encapsulate a lot of the semantics of songs. Such information could help users navigate easily through a large collection of songs and to recommend new music to them. However, this information is often unavailable in its textual form. To get around this problem, singing voice recognition systems could be used to obtain transcripts directly from the audio. These approaches are generally adapted from the speech recognition ones. Speech transcription is a decades-old domain that has lately seen significant advancements due to developments in machine learning techniques. When applied to the singing voice, however, these algorithms provide poor results. For a number of reasons, the process of lyrics transcription remains difficult. In this thesis, we investigate several scientifically and industrially difficult ’Music Information Retrieval’ problems by utilizing lyrics information generated straight from audio. The emphasis is on making approaches as relevant in real-world settings as possible. This entails testing them on vast and diverse datasets and investigating their scalability. To do so, a huge publicly available annotated lyrics dataset is used, and several state-of-the-art lyrics recognition algorithms are successfully adapted. We notably present, for the first time, a system that detects explicit content directly from audio. The first research on the creation of a multilingual lyrics-toaudio system are as well described. The lyrics-toaudio alignment task is further studied in two experiments quantifying the perception of audio and lyrics synchronization. A novel phonotactic method for language identification is also presented. Finally, we provide the first cover song detection algorithm that makes explicit use of lyrics information extracted from audio
Tang, Xiaoming. "Contribution à la conception des systèmes à base de connaissances temps réel pour l'aide au contrôle de procédés continus." Valenciennes, 1989. https://ged.uphf.fr/nuxeo/site/esupversions/14214bfb-9aa9-4aef-b529-a5014cdbc3f6.
Doras, Guillaume. "Automatic cover detection using deep learning." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS299.
Covers are different interpretations of the same original musical work. They usually share a similar melodic line or harmonic structure, but typically differ greatly in one or several other dimensions, such as structure, tempo, key, instrumentation, genre, etc. Automatic cover detection – the task of finding and retrieving from an audio corpus all covers of one or several query tracks – has long been seen as a challenging theoretical problem. It also became an acute practical problem for with the ever-growing size of modern audio corpora.In this work, we propose to address the cover detection problem with a solution based on the metric learning paradigm. We show that this approach allows training of simple neural networks to extract out of a song an expressive and compact representation – its embedding – suitable for fast and effective retrieval in large audio corpora. We then propose a comparative study of different audio representations and show that systems combining melodic and harmonic features drastically outperform those relying on a single input representation. We illustrate how these features complement each other with both quantitative and qualitative analyses. We describe various fusion schemes and propose methods yielding state-of-the-art performances on publicly available large datasets. Finally, we describe theoretically how the embedding space is structured during training, and introduce an adaptation of the standard triplet loss which improves the results further. We finally describe an operational implementation of the method, and demonstrate its efficiency both in terms of accuracy and scalability in a real industrial context