Synchronous visualization of multimodal measurements on lips and glottis: Comparison between brass instruments and the human voice production system
Bibliographic record
Abstract
Brass instruments and the human voice production system are both composed of a vibrating “human valve” (constriction in a pipe) coupled to an acoustic resonator: lips coupled to the brass instrument or vocal folds coupled to the vocal tract. In both cases, the aeroacoustic coupling is responsible for the self-oscillations and the large variety of regimes. Additionally, brass instruments and voice share difficulties for the in-vivo measurement of the exciter activity. Hence, the development of a common tool is relevant. It is also relevant to explore the effect of some known differences between these systems, namely, the strength of the coupling and the physiological characteristics. This paper introduces components for the development of such a tool. First, two corpuses of multimodal measurements are presented: one for a singer's larynx during sustained vowels, one for a musician's lips during sustained notes. They include high-speed video (HSV) recordings, electrical impedance (EI) measurements (electrolabiograph/electroglottograph), and audio recordings (AR). Then, we introduce two estimation algorithms: (AW) one of the opening area waveforms from videos, (LF) one of the LF-model parameters on these waveforms. Moreover, we build a video tool displaying, synchronously, the signals (HSV), (AW), (EI), and (AR) in time and frequency domains. Finally, this tool is exploited to exhibit common behaviors and relevant differences between brass instruments and human voice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".