On Multichannel Coherent-to-Diffuse Power Ratio Estimation
Bibliographic record
Abstract
The significance of the coherent-to-diffuse-power ratio (CDR) has grown in the fields of speech dereverberation and noise reduction. However, the existing CDR estimators are typically limited to applications with only two microphones. In this article, we investigate CDR estimation in multichannel acoustic systems with more than two microphones. We propose two estimation methods. The first approach involves decomposing the microphone array into several groups of subarrays, where each subarray consists of only two sensors. We estimate the CDR for each group and then fuse these group CDR estimates through weighted averaging to form the multichannel CDR estimate. This weighted-average CDR estimation can be seen as an extension of traditional two-channel CDR estimation methods to the multichannel scenario. The second method is based on array manifold estimation using a joint matrix diagonalization technique, eliminating the need for subarray decomposition. By integrating the CDR estimates with a parametric Wiener-type postfilter, we demonstrate, via simulations, the superior performance of the proposed techniques in terms of CDR estimation accuracy, signal-to-noise ratio (SNR) gain, log-spectral distortion (LSD), and direct-to-reverberant-energy ratio (DRR).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".