Bibliographic record
Abstract
In music, dissonance is the unpleasant quality of sound. While dissonance is generally regarded as arising from two notes played simultaneously, low frequency notes sound dissonant when played alone on a piano. I hypothesize that this dissonance arises from the harmonics of the played note interfering with one another. Using the piano as a model, perceived dissonance was calculated through the combination of the two-tone dissonance formula with the A-weighting curve and the different harmonic intensities of a piano. Spectrographs of sample piano notes were used to compare harmonics of low frequency and high frequency notes.Single note dissonance increased rapidly as note frequency decreased. A-weighting had no qualitative effect on the dissonance-frequency trend, implying a physical and not a psychoacoustic cause. As verified in the spectrographs, the lower register note had harmonics closer together, compared to the higher register note. It is thus possible that the harmonics of low notes interfere significantly with each other, thereby producing the observed single-note dissonance.The simulation produces a score for the perceived dissonance of a single-note played on the piano. This analysis could be adapted in the future to other instruments, including aerophones, as well as integrate timbre, partials, and inharmonics. La dissonance musicale et pour la plupart compris en matière de deux notes simultanées. Cependant, les notes de basse fréquence semble en désordre quand ils sont joués seules sur un piano. L’explication proposée dans cet oeuvre est que cette dissonance se produit des harmoniques des notes qui s’interfèrent. Utilisant le piano comme exemple, la dissonance perçue a été calculé par la combination de la formule de dissonance entre deux notes avec la courbe de pondération A et les intensités harmoniques d’un piano. Les spectres sonores des notes de piano ont été utilisés pour la comparaison des notes de basse et haut fréquences. La dissonance des seules notes augmentait rapidement alors que les fréquences des notes diminuaient. La pondération A n’avait pas eu un effet sur la tendance entre la dissonance et les fréquences, qui signifie la présence d’une cause physique au lieu d’une cause auditive. Vérifié dans les spectres sonores, la note de registre inférieur avait ses harmoniques plus proches comparés à la note du registre plus haut. C’est alors possible que les harmoniques des notes des registres inférieurs interfèrent considérablement avec les unes les autres, et produit ainsi la dissonance des seules notes observée dans les calculs. La simulation produit un résultat pour la dissonance perçue sur un piano. Cette analyse pourrait être adaptée dans le futur aux autres instruments, incluant les aérophones, et puis intégrer aussi des analyses de timbre, des tons simples, et des inharmoniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".