Acoustic Reflections Detection for Microphone Array Applications
Why this work is in the frame
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Bibliographic record
Abstract
In hands-free audio conferencing applications, microphone arrays [1] [2] using simple beamforming technique often cannot differentiate between the scenarios where (1) a single talker speaking in the presence of acoustic reflections, and (II) two individual talkers having a conversation. Various approaches have been proposed tosolve this problem [1]-[6]. However, most of these approaches are either beamforming technique specific or computational demanding. In this study, we proposed a new method that uses the maximum correlation lag to distinguish a new talker from acoustic reflections. The proposed method considers the power of beamformer outputs instead of raw microphone signals. Therefore, the proposed method allows any beamforming technique to be employed, and at the same time, lowers the computational requirement making a real time implementation possible. Experimental results performed in an anecholc chamber and in a reverberant room show that scenario I always has a greater amount of maximum correlation lag, whereas scenario II always results in less correlation lag. These results in less correlation lag. These results suggest that correlation lag could be used as an effective means for distinguishing talkers from reflections.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it