Improving the speech intelligibility of forensic audio recordings through adaptive filtering with non-synchronous interference signals.
Bibliographic record
Abstract
Forensic audio recordings are frequently made in uncontrollable acoustic environments where background sound emanating from television, radio, and video or music playback may interfere with the intelligibility of the intended “target” speech on a recording. In such cases, adaptive filtering techniques have proven highly effective in eliminating the interfering sound sources and improving intelligibility, provided that the interfering reference signal was acquired simultaneously with the target speech. However, in cases where interfering signals are acquired through a post hoc retrieval of broadcast, music, or video recordings, non-linear time base differences between the original and the secondarily acquired reference may significantly lessen the effectiveness of conventional adaptive filtering techniques in improving speech intelligibility. The current paper describes the results in applying a commercially available, adaptive filtering tool as well as a newly developed tool, drift-compensated adaptive filtering (DCAF), for improving the intelligibility of recorded speech when utilizing a non-synchronously acquired reference signal. Listening tests show an overall improvement in speech intelligibility through the application of adaptive filtering with non-synchronous reference signals, with greater intelligibility for DCAF-processed audio recordings as compared to recordings processed with conventional adaptive filtering techniques.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".