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Experimental music for experimental physics

2014· article· en· W2278897983 sur OpenAlexaboutno aff
Rosaria Marraffino

Notice bibliographique

RevueCERN Document Server (European Organization for Nuclear Research) · 2014
Typearticle
Langueen
DomaineComputer Science
ThématiqueMusic Technology and Sound Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhysicsTheoretical physics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Using the sonification technique, physicist and composer Domenico Vicinanza paid homage to CERN at its 60th anniversary ceremony. After months of hard work, he turned the CERN Convention and LHC data into music. Click here to download the full score of the "LHChamber music". Every birthday deserves gifts and CERN’s 60th anniversary was no exception. Two gifts were very special, thanks to the hard work of Domenico Vicinanza, a physicist and composer. He created two experimental pieces by applying the sonification technique to the CERN Convention and to data recorded by the four LHC detectors during Run 1. “This technique allows us to ‘hear’ data using an algorithm that translates numbers or letters into notes. It keeps the same information enclosed in a graph or a document, but has a more aesthetic exposition,” explains Domenico Vicinanza. “The result is meant to be a metaphor for scientific cooperation, in which different ‘voices’ and perspectives could reach the same goal only by ‘playing’ together.” Each source of data could be sonified in many ways, which is where the genius of the composer comes in: “I chose how to map the data to musical parameters since, as with any composition, I had to make decisions concerning the choice of the scale, the instruments, which instruments play when, timbre, tempo, etc.,” says Vicinanza. “I created different melodic lines but the magic happened when all the instruments played together. The same applies to scientific research, where support and cooperation allow great results to be achieved.” The Convention has more than 28,000 characters, and each one was translated into a note: same letter, same note. The algorithm didn’t do all the work; the human element of the creative process was essential: “It took me more than 250 hours to sonify the Convention, but I really wanted to celebrate what it really means: peace beyond any political disagreement, for the common purpose of scientific research,” explains the composer. During the 60th anniversary ceremony, the European Union Youth Orchestra* directed by Maestro Vladimir Ashkenazy, played Domenico Vicinanza’s sonified Convention, so he had to write the score for each instrument. "The orchestra kindly accepted to extend their membership, which originally represented the 28 EU Member States, to 42 young players covering all of CERN’s Member States and Observers in order to convey the message of harmony and peace that they share with our Organization" says Paola Catapano of CERN’s Communications Group, co-producer, with Vicinanza, of the EUYO performance. For the LHChamber music, seven instruments were recorded in the four experimental caverns and in the CERN Control Centre. “Scientists played the music from the data they had worked on in the places where they actually worked,” explains Vicinanza. “Every instrument had its own sense, like the research they did, but only when they’re played together do you feel the harmony in it: it’s like the whole goes beyond the sum of the parts. Writing the scores out of these data made me feel like the musical alter ego of a researcher.” * Click here to download the mp3 recording of the European Union Youth Orchestra. Did you know? “If the Large Hadron Collider made music, what would it sound like?” read the title of an article in The Guardian on 30 September featuring the “LHChamber Music” video. This was just one of 36 online media sources across the world, including the UK, China, India, the US, Italy, France, Canada and even Palestine, that reported on CERN’s You Tube video between September 29 and October 4, reaching a combined readership of more than 198 million people! With hits on the YouTube video growing from 9000 to over 20,000 views in under 24 hours and reaching over 50,000 in one week, the video is definitely CERN’s most successful in 2014 and among the top ten ever published on the CERN YouTube channel. “LHChamber Music” went viral on Twitter too: the phrase “CERN music” was tweeted 1147 times in nine days by 1039 contributors, including unexpected CERN supporters such as the UK’s Royal Philharmonic Society!

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,019
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,223
Score d'incertitude au seuil0,745

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,019
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0040,006
Communication savante0,0090,010
Science ouverte0,0020,007
Intégrité de la recherche0,0050,008
Charge utile insuffisante (le modèle a refusé de juger)0,2230,094

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,280
Écart entre enseignants0,243 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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