Single Channel Speech Separation using Minimum Mean Square Error Estimation of Sources' Log Spectra
Bibliographic record
Abstract
We present an approach for separating two speech signals when only one single recording of their linear mixture is available. The log spectra of the sources are estimated from the mixture's log spectrum using minimum mean square error (MMSE) approach. The estimation is obtained from the assumption that the sources are modelled using a set of Gaussian subsources which are related to the mixture using MIXMAX approximation. The resulting estimator has a closed form and is expressed using the mean and variance of Gaussian subsources. In order to obtain the two most likely subsources which generate the mixture, we use the estimation-detection technique. We also show that the binary mask filtering which has been empirically - and with no mathematical justification - used in speech separation techniques is, in fact, a simplified form of the MMSE estimator. The proposed technique is compared with the binary mask when the input consists of male-male, female-female, and female-male mixtures. The experimental results in terms of segmental SNR show that the MMSE estimator outperforms binary mask filtering.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".