Long-Term Gain Estimation in Model-Based Single Channel Speech Separation
Bibliographic record
Abstract
Model-based single channel speech separation techniques commonly use trained patterns of the individual speakers to separate the speech signals. In most recent proposed techniques, it is assumed that data used in the train and test phase have the same level of energy, a prerequisite which is hardly met in the real situations. Considering this limitation, we propose a technique which estimates the gain associated with the individual speakers from the mixture and thus obviate the need for this assumption. The basic idea is to express the probability density function (PDF) of the mixture in terms of the individual speakers' PDFs and corresponding gains. Then, those patterns and gains which maximize the mixture's PDF are selected and used to recover the speech signals. Experimental results conducted on a wide variety of mixtures with signal-to-signal ratios ranging from 0 to 18 dB show that the proposed technique estimates the speakers' gain with 95% accuracy within the range of the actual gain ±%20. Comparing the separated speech signals with the original ones in terms of SNR criterion with/without including the gain estimation stage, we observe a significant SNR improvement (on average 5.73 dB) for the gain included scenario.
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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 | 0.001 |
| 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".