Notice bibliographique
Résumé
The playing of a musical instrument is one of the most skilled and complex interactions between a human and an artifact. Professional musicians spend a significant part of their lives initially learning their instruments and then perfecting their skills. The production, distribution and consumption of music has been profoundly transformed by digital technology. Today music is recorded and mixed using computers, distributed through online stores and streaming services, and heard on smartphones and portable music players. Computers have also been used to synthesize new sounds, generate music, and even create sound acoustically in the field of music robotics. Despite all these advances the way musicians interact with computers has remained relatively unchanged in the last 20-30 years. Most interaction with computers in the context of music making still occurs either using the standard mouse/keyboard/screen interaction that everyone is familiar with, or using special digital musical instruments and controllers such as keyboards, synthesizers and drum machines. The string, woodwind, and brass families of instruments do not have widely available digital counterparts and in the few cases that they do the digital version is nowhere as expressive as the acoustic one. It is possible to retrofit and augment existing acoustic instruments with digital sensors in order to create what are termed hyper-instruments. These hyper-instruments allow musicians to interact naturally with their instrument as they are accustomed to, while at the same time transmitting information about what they are playing to computing systems. This approach requires significant alterations to the acoustic instrument which is something many musicians are hesitant to do. In addition, hyper-instruments are typically one of a kind research prototypes making their wider adoption practically impossible. In the past few years researchers have started exploring the use of non-invasive and minimally invasive sensing technologies that address these two limitations by allowing acoustic instruments to be used without any modifications directly as digital controllers. This enables natural human-computer interaction with all the rich and delicate control of acoustic instruments, while retaining the wide array of possibilities that digital technology can provide. In this chapter, an overview of these efforts will be provided followed by some more detailed case studies from research that has been conducted by the author's group. This natural interaction blurs the boundaries between the virtual and physical world which is something that will increasingly happen in other aspects of human-computer interaction in addition to music. It also opens up new possibilities for computer-assisted music tutoring, cyber-physical ensembles, and assistive music technologies.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».