Performance of GCC- and AMDF-Based Time-Delay Estimation in Practical Reverberant Environments
Bibliographic record
Abstract
Recently, there has been an increased interest in the use of the time-delay estimation (TDE) technique to locate and track acoustic sources in a reverberant environment. Typically, the delay estimate is obtained through identifying the extremum of the generalized cross-correlation (GCC) function or the average magnitude difference function (AMDF). These estimators are well studied and their statistical performance is well understood for single-path propagation situations. However, fewer efforts have been reported to show their performance behavior in real reverberation conditions. This paper reexamines the GCC- and AMDF-based TDE techniques in real room reverberant and noisy environments. Our contribution is threefold. First, we propose a weighted cross-correlation (WCC) estimator in which the GCC function is weighted by the reciprocal of AMDF. This new method can sharpen the peak of the GCC function, which corresponds to the true time delay and thus leads to a better estimation performance as compared to the conventional GCC estimator. Second, we propose a modified version of the AMDF (MAMDF) estimator in which the delay is determined by jointly considering the AMDF and the average magnitude sum function (AMSF). Third, we compare the performance of the GCC, AMDF, WCC, and MAMDF estimators in real reverberant and noisy environments. It is shown that the AMDF estimator can yield better performance in favorable noise conditions and is slightly more resilient to reverberation than the GCC method. The GCC approach, however, is found to outperform the AMDF method in strong noisy environments. Weighting the correlation function by the reciprocal of AMDF can improve the performance of the GCC estimator in reverberation conditions, yet its improvement in noisy environments is limited. The MAMDF algorithm can enhance the AMDF estimator in both reverberant and noisy environments.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".