A frog in your throat or in your ear? Searching for the causes of poor singing.
Bibliographic record
Abstract
Singing is a cultural universal and an important part of modern society, yet many people fail to sing in tune. Many possible causes have been posited to explain poor singing abilities; foremost among these are poor perceptual ability, poor motor control, and sensorimotor mapping errors. To help discriminate between these causes of poor singing, we conducted 5 experiments testing musicians and nonmusicians in pitch matching and judgment tasks. Experiment 1 introduces a new instrument called a slider, on which participants can match pitches without using their voice. Pitch matching on the slider can be directly compared with vocal pitch matching, and results showed that both musicians and nonmusicians were more accurate using the slider than their voices to match target pitches, arguing against a perceptual explanation of singing deficits. Experiment 2 added a self-matching condition and showed that nonmusicians were better at matching their own voice than a synthesized voice timbre, but were still not as accurate as on the slider. This suggests a timbral translation type of mapping error. Experiments 3 and 4 demonstrated that singers do not improve over multiple sung responses, or with the aid of a visual representation of pitch. Experiment 5 showed that listeners were more accurate at perceiving the pitch of the synthesized tones than actual voice tones. The pattern of results across experiments demonstrates multiple possible causes of poor singing, and attributes most of the problem to poor motor control and timbral-translation errors, rather than a purely perceptual deficit, as other studies have suggested.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".