On time delay estimation based on multichannel spatiotemporal sparse linear prediction
Bibliographic record
Abstract
Noise and reverberation can significantly affect the performance of time delay estimation (TDE) in room acoustic environments. The multichannel cross-correlation coefficient (MCCC) algorithm, which extends the traditional cross-correlation method from two to multiple channels, can exploit the spatial information among multiple microphones to improve the robustness of TDE with respect to environmental noise; but this algorithm is not robust to reverberation. The multichannel spatiotemporal prediction (MCSTP) algorithm uses both the spatial and temporal information provided by the array. This algorithm improves significantly the robustness of TDE with respect to reverberation; however, it is found sensitive to noise. In this paper, we develop a multichannel spatiotemporal sparse prediction (MCSTSP) algorithm for TDE. This algorithm obtains a good compromise between robustness of TDE to noise and that to reverberation through making a tradeoff between pre-whitening and non-prewhitening. This is achieved via adjusting a regularization parameter, which is solved by an augmented Lagrangian alternating direction method of multipliers (ADMM). The property of this developed algorithm is justified with numerical experiments in both noisy and reverberant environments.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".