Time-Frequency Analysis and Its Applications to Multimedia Signals
Bibliographic record
Abstract
"Time-frequency analysis has been intensively investigated and developed in the last two decades. A variety of time-frequency distributions have been developed to provide efficient analysis of signals with a time-varying spectral content. In most cases, signal analyses in the joint time-frequency domain outperform the traditional frequency-domain approaches. Generally, the time-frequency distributions have found fruitful applications in many important fields dealing with nonstationary signals, such as biomedical, radar, seismic, telecommunications, and mechanical engineering. Additionally, a large number of applications are related to multimedia signals in speech, audio/music, image, and video signal processing, where time-frequency analysis can be employed to broaden and enhance the signal processing capabilities. Because each type of multimedia signals has its specific nature that may significantly differ from others, the applicability and method of time-frequency analysis depend on the multimedia data to be processed. This fact opens a number of challenging directions for research in the field of time-frequency analysis and its applications to multimedia signals. For instance, having the different dimensionalities of multimedia signals in mind, time-frequency analysis for one-, two-, and three-dimensional signals should be used." -- from p1.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.003 |
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".