Incorporating image sources in the time-domain beamforming model for the localization of sound sources in reverberant rooms
Bibliographic record
Abstract
In this study, we use a small hemispherical microphone array to locate impulsive or stationary sound sources in reverberant rooms. A modified version of the conventional time-domain beamforming model was used, which incorporates physical information from the early specular reflections of the sound source, as well as the geometric and absorption characteristics of the room. In other words, the propagation model takes into account the potential image sources up to a given image order. We show that the technique can be seen as a time-reversal process, the measured pressure field being simultaneously re-emitted from both the actual array position and its virtual image positions. We then present results applying this modified model to simulated data (generated using an image-source propagation model), as well as measurements in real rooms, using both impulsive and continuous broad-band noise. In all cases, the modified model shows an improvement in localization over conventional beamforming. The issue of appropriately adjusting the image order and mixing time when performing the processing for optimal results is also addressed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".