LSTCM: Long-Term and Short-Term Transform of Convolutive Model in the STFT Domain
Bibliographic record
Abstract
This paper investigates the convolutive transfer function (CTF) model in the short-time Fourier transform (STFT) domain. The CTF model depends on both the analysis window length and the step size between adjacent frames. We introduce an interpolation process for the source signal in the STFT domain, expressing the source signal as an interpolation of multi-frame STFT-domain signals. Based on this interpolation, we derive a CTF model, where CTF coefficients are proportional to the Fourier transform of the windowed impulse response. Notably, the window is independent of the STFT analysis window. We propose the LSTCM approach, which transfers CTF coefficients between different window lengths and step sizes. The LSTCM consists of two parts: the decoding process and the recoding process. The decoding process converts CTF coefficients into time-domain impulse responses by utilizing frequency band results from the Fourier transform of the upsampled CTF coefficients, concatenating these results, and applying the inverse Fourier transform. The recoding process translates the time-domain impulse response back into CTF coefficients for the target window length and step size. Simulations indicate that an overlap rate greater than 75% between adjacent frames is necessary for an accurate model. To demonstrate the potential of the proposed LSTCM framework, we apply it to establish a connection between a long-term source separation approach and a short-term noise reduction method in the STFT domain. The long-term source separation generates estimates of impulse responses, while the LSTCM builds the accurate model in the short-term STFT domain, leading to a multiple-input/output-inverse-theorem (MINT) filter and a Wiener filter derived from the model parameters. The results illustrate the significant potential of the LSTCM.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".