High speed imaging and measurement of laryngeal vibration during phonation using ultrafast ultrasonography: A preliminary study
Bibliographic record
Abstract
Observation and measurement of laryngeal vibration during phonation is essential for study of voice production. Due to the advantages in noninvasiveness and penetration, conventional B mode ultrasonography has been introduced in our previous studies of laryngeal tissue vibration. However, its main drawbacks are the insufficient frame rate, drastically narrowed field of view(FOV) and line to line acquisition lag. Thus, ultrafast ultrasonography that offers a much wider FOV and ultrahigh frame rate is introduced in the present study for observation and measurement of laryngeal vibration. Ultrafast ultrasonography is achieved by emitting a plane wave using the full aperture of a linear array transducer on a scanner(SonixTouch, Ultrasonix, Canada) and then applying beamforming on received raw RF data. The FOV covers the whole glottis as well as sub- and supraglottal structures of the larynx. Non-stationary laryngeal vibration are recorded at 5000 fps when subjects start voicing vowel /u:/ during experiments. To measure the vibration, a RF speckle tracking algorithm based on normalized cross correlation is used. The cross correlation coefficient between the displaced and best matched reference speckle is also obtained at each point within the region of interest. The electroglottogram(EGG) is recorded as a reference indicator of vibration phase. The vibration of the vocal fold can be easily identified and shows clear correlation with EGG waveform. The non-stationary process of the vibration of sub and supra glottal tissue during onset of voicing is well quantified with high temporal resolution, while the vibration of the body and cover of the vocal fold appears chaotic and against mechanical principle. Measurement of vibration of the vocal fold body is compromised by its low signal-to-noise ratio. Measurement of vibration of the vocal fold edge is compromised by severe signal decorrelation. Nevertheless, ultrafast ultrasonography still can be used in visualization and measurement of the vibration and deformation of certain structures in the larynx. Thus, there is potential value of this technique being used in estimation of mechanical properties of laryngeal tissue.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".