Variational Probabilistic Speech Separation Using Microphone Arrays
Bibliographic record
Abstract
Separating multiple speech sources using a limited number of noisy sensor measurements presents a difficult problem, but one that is of great practical interest. Although previously introduced source separation methods [such as independent component analysis (ICA)] can be made to work in many situations, most of these methods fail when the sensors are very noisy or when the number of sources exceeds the number of sensors. Our approach to this problem is to combine the multiple sensor likelihoods [obtained using time-delay-of-arrival (TDOA) information] with a generative probability model of the sources. This model accounts for the power spectrum of each source using a mixture model, and accounts for the phase of each source using one discretized hidden phase variable for each frequency. Source separation is achieved by identifying the source vector configuration of maximum a posteriori probability, given all available information. An exhaustive search for the MAP configuration is computationally intractable, but we present an efficient variational technique that performs approximate probabilistic inference. For the problem of separating delayed additive noise corrupted speech mixtures, the algorithm is able to improve upon the signal-to-noise ratio (SNR) gain performance of existing state-of-the-art probabilistic and TDOA-based speech separation algorithms by over 10 dB. This significant performance improvement is obtained by combining the information utilized by these approaches intelligently under a representative probabilistic description of the speech production and mixing process. The method is capable of recovering high fidelity estimates of the underlying speech sources even when there are more sources than microphone observations
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".