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Geheimnisvolle Musik

2010· article· en· W6991146984 sur OpenAlexaboutno aff

Notice bibliographique

RevueScholarship@Western (Western University) · 2010
Typearticle
Langueen
DomaineComputer Science
ThématiqueMusic and Audio Processing
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNothingSubject (documents)MusicalMusic industryPopular musicFell
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Looking beyond tradtional genre categorizations, this blog ties together strands of similarities among diverse types of music. As we enter the second decade of the 21st Century, people seem more open to different types of music than in previous times. Or, they at least admit to it more readily. Furthermore, many musicians and composers have engaged with genres beyond the ones with which they are most commonly identified. Still, defining one’s tastes by genre remains firmly established for any number of reasons. If nothing else, it can provide shorthand for identifying one’s status, or it can aid with marketing products to specific demographic groups.\nThe confines of genre also remain a problem in sites that recommend music to users. Even to those who have diverse musical tastes, Amazon tends to make fairly safe recommendations while ignoring deeper similarities that might exist among specific works. Vendors are not alone, as music social networking sites do the same thing. Interestingly enough, it isn’t due to genre itself acting as a criterion. Rather, a number of websites draw upon ”collaborative filtering” algorithms that tend to skew towards genre.\nA 2009 study by Neal et al analyzed the top 10 pieces of music tagged with five emotional states (Happy, Sad, Anger, Disgust, Fear) on last.fm. All 50 of them fell under the umbrella term “popular” music. Although the definition of “popular” is subject to debate (at least considering various usages of the term), it is worth noting that pieces from other broad genres (such as classical, jazz, and ”international” music) remained absent from the top ten results for all emotion-based tags. This alone may not indicate the pervasiveness of genre in defining musical tastes, but the results seem to indicate that the user base of last.fm skews towards more popular types of music.\nStudies and Prospects:\nUntil more sophisticated music recommendation systems become ubiquitous, it seems suitable to give holistic consideration to other musical and extramusical facets that could maximize their potential. A poster presentation for the 2009 American Society for Information Science and Technology (ASIST) conference in Vancouver was a preliminary attempt at envisioning such systems. Its companion paper provides a brief background and prospective theoretical framework as a guide for such an endeavour. For a more substantial examination of such prospects, read “Precedent or Preference? The Construction of Genre and Music Recommender Systems” (2012, pp. 15-39). It is available in print, and on Google Books.\nIt also endeavours to synthesize relevant ideas from many areas of study. Leonard Bernstein’s exploration and embrace of universality in musical language provides a crucial historical antecedent to the notions explored here. Another influence is more recent work by New Yorker music critic Alex Ross (especially his 2007 book The Rest is Noise, as well as his blog of the same name). To complement the humanities perspective, technology can aid in the development of music retrieval systems that transcend genre. A system could draw upon the musical facets outlined by J. Stephen Downie, or it could have social networking aspects where laypersons share hunches about similarities among seemingly different pieces of music. Systems-level work by such researchers as Paul Lamere and Stephan Baumann seems promising for de-emphasizing genre as a way of categorizing music. Nonetheless, progress has remained limited due to relatively small sample sizes, and because few people have actively engaged in such research.\nGenre cross-pollination and transcendence is not a new concept to me, either. Although I have given it vague thought over the years, only recently have I begun thinking more closely about the implications. An upcoming series of postings, something like a musical autobiography, will outline how my musical tastes have led me to focus more closely on this topic.\nIf nothing else, I’m hoping that my own modest contributions will aid in helping people rethink the boundaries of genre in music. I invite you to join me in this journey, whether as a reader or a contributor.\nContent of Postings:\nThis blog aspires towards analysis, rather than “hot” news briefs (which you’ll find elsewhere, anyway) and rumours to grab attention. My domain of knowledge and interests is also highly idiosyncratic, so don’t expect commentary on every “major” news story from every genre. My interests, understandings, and domain of knowledge are simply a foundation for further discussion on the commonalities found among diverse kinds of music.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,067
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,004
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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 Opus0,079
Tête enseignante GPT0,300
Écart entre enseignants0,221 · 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 tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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
Publié2010
Routes d'admission1
Résumé présentoui

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