Subband blind source separation for convolutive mixture of speech signals based on dynamic modeling
Bibliographic record
Abstract
In this paper, a subband blind source separation method based on dynamic modeling for convolutive mixture of speech signals is proposed. We show that by applying a dynamical model to subband signals, some of the drawbacks of the time domain approach can be resolved, leading to improvements in separation performance. By employing the subband processing, we enhance the speed of the method, first by reducing the computational cost of the algorithm resulting from shorter demixing filters and second, by considering the parallel processing capability of the subband domain. Furthermore, by applying particular settings to the step-size parameter and to the demixing filter lengths in various subbands, we achieve much better performance in terms of the separation ability. The proposed algorithm is applied to two different experiments and a comparison is done against the time domain approach. The results demonstrate the superiority of the subband domain in terms of speed and accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".