Parametric time-frequency analysis and its applications in biomedical and multimedia
Bibliographic record
Abstract
Analysis of non-stationary signals is a challenging task. The purpose of this thesis is to explore an efficient and powerful technique to analyze and classify two types of non-stationary signals, that is, multimedia signals in higher frequency range (44.1 kHz) and biomedical signals in lower frequency range (2 kHz). An adaptive true non-stationary time -frequency signal analysis tool - matching pursuit, is introduced and applied to decompose the sample signals into time-frequency functions or atoms. Atom parameters are analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using matching pursuit, several additional features, such as central energy and octave activeness ratio, are also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. In the 6-group classification of 96 pieces of 5-second music signals, such as, christmas choir, country, greek music, jazz, rock and scottish music, the accuracy reaches 89.6%, when the feature set includes standard deviation of octave (the scale factor which controls the width of the window function), median of octave, standard deviation of innerProdI (imaginary part of the inner-product between the signal and the atom), standard deviation of realGG (real part of the inner-product between the complex atom and its conjugate), and central energy. For the database of 112 pieces of 10-second music sugnals, the 2-group classification (rock-like and classical-like) accuracy achieves 100%, having a standard deviation of octaves in the first 2,000 atoms as the discriminant feature. An accuracy of 74.2% is obtained for the 2-group knee sound signal classification, and optimum feature set comprises octave activeness ratio, central energy and standard deviation of innerProdI. From our experiments, it is evident that the matching pursuit algorithm with the Gabor dictionary decomposes non-stationary signals, including multimedia signals in higher frequency and biomedical signals in lower frequency ranges, into atoms whose parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".