Using Polymeric Piezoelectric Accelerometers to Measure Vocal Pitches and Tones
Bibliographic record
Abstract
Human vocal pitches and tones contain vital information with vast applications. Combined with artificial intelligence, they can be used for noninvasive diagnosis of larynx diseases or synthesis of artificial voice for people with disabilities. The development of relevant applications has been a heated research topic. An essential basis of these novel applications is the reliable sensing of human vocal signals. With this respect, due to their unique advantages, such as ambient noise isolation, good dynamic response, and high linearity, piezoelectric accelerometers are promising candidates besides microphones to measure human vocal pitches and tones. Their application potential is further enhanced when developed using polymers, making them more suitable as disposable wearable sensors for medical systems. This article uses polymeric piezoelectric accelerometers as wearable sensors to collect human vocal pitches and tones by measuring larynx vibration on the neck surface. The polymeric piezoelectric accelerometers developed in this article have an average resonant frequency of 968.33 Hz, a 5% flat bandwidth of 380 Hz, an in-band sensitivity of 4.99 pC/g, and an in-band noise floor of$3.7~\mu $g/$\surd $Hz. The data collected by these accelerometers on the neck for different words can be played into corresponding sounds without complex post-measurement processing, demonstrating their capability to collect vocal pitches and tones accurately by wearable vibration measurement and validating a novel application direction for polymeric sensors in the era of artificial intelligence.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".