Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an efficient mechanism for the fusion of two noisy speech signals obtained by an array of two microphones using single-tap time-frequency filters and by taking into account the correct time delay of arrival (TDOA) of the speech source. Speech signals obtained by the microphones are transformed into a set of two complex time-frequency (TF) images. By knowing the correct TDOA, and therefore the associated phase difference between the signals at each frequency, it is possible to non-linearly filter both the real and the imaginary parts of the TF images. This will consist of a TF reward-punish filter that adjusts the amplitude of the TF blocks based upon the variation of their phase-difference with the ideal phase-difference defined by the TDOA. Simulation results show that the proposed technique can achieve a Signal-to-Noise Ratio (SNR) improvement of 15 dB when there, is strong Gaussian noise present (-20 dB initial SNR). When the original SNR is OdB, the simulated improvement is approximately 8 dB. It is also shown that although the proposed technique is a more general case of the adaptive beamformer (where the adaptive beamformer has a specific reward-punish characteristic), other reward-punish characteristics that are proposed in this paper can often surpass the performance of the ideal adaptive beamformer.
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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 it