Switching Kronecker Product Linear Filtering for Multispeaker Adaptive Speech Dereverberation
Bibliographic record
Abstract
Dereverberation, a process to mitigate or eliminate the reverberation effect, plays an important role in hands-free speech communication and human-machine interfaces. Tremendous efforts have been devoted to this problem and various methods have been developed over the last three decades. Those methods generally assume that there is only a single speaker in the acoustic environment and, consequently, they suffer from significant performance degradation if multiple speakers participate in the conversation. How to deal with reverberation in multiple-speaker scenarios is still a challenging problem, which is studied in this work. We present a switching multichannel linear prediction filtering method, which designs multiple linear filters with each tracking one speaker. When some speaker is active, the corresponding filter and the weighted cross-correlation matrix are updated while the other filters are kept unchanged. To further improve the performance and reduce complexity, we apply the Kronecker product to decompose every linear prediction filter into a Kronecker product of two shorter filters: one is time-invariant and the other is time-varying. The former is estimated with a batch method (using only a few seconds of speech signal when the corresponding speaker starts to talk in the entire conversation) while a recursive least-squares algorithm is derived for identifying the time-varying set of Kronecker filters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".