Tonality of Low-Frequency Synthesized Piano Tones
Bibliographic record
Abstract
The influences of inharmonicity and bandwidth on sensitivity to tonality in the low frequency range (A0 to G#1) were tested in a listening experiment. Participants were presented a key-defining context (do-mi-do-so) and were asked to rate the goodness of fit of probe tones to the context. Probe tones were the 12 tones of the chromatic scale beginning on do. The set of 12 ratings, called the probe-tone profile, was compared to an established standardized profile for the Western tonal hierarchy. Prior research employing this method with real (sampled) piano tones has suggested that sensitivity to tonality is influenced by inharmonicity, particularly in the lowest octaves of the piano where inharmonicity levels are substantially above the detection threshold. In the present experiment, sensitivity to tonality was tested using synthesized piano-like tones that were either harmonic or inharmonic. Participants were tested in either a broadband (no filtering) or low-pass (low-pass filtered at 1 KHz) condition. Sensitivity to tonality was highest in the broadband harmonic condition followed by the broadband inharmonic condition. No sensitivity to tonality was found for the low-pass conditions; rather, for both harmonic and inharmonic tones, participants rated probe tones as increasingly good fit as pitch distance from do decreased. Keywords: musical pitch, tonality, probe-tone technique, inharmonicity, piano acoustics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".