Acoustic Beamforming via Interference-Plus-Noise Covariance Matrix Construction for Interferences and Noise Attenuation
Bibliographic record
Abstract
The interference-plus-noise covariance matrix (INCM) is essential in improving an acoustic beamformer's interference and noise attenuation performance. In practical implementation, INCM reconstruction is required to remove the signal of interest (SOI) components from the sample covariance matrix. However, some of the interference and noise components are inevitably removed during the INCM reconstruction process to avoid distortion of the desired speech, which deteriorates the interference and noise attenuation performance of the beamformer. This paper proposes constructing an INCM with as much information on the interferences and noise as possible by adding covariance matrices of the spherically diffuse noise, background noise, and interferences. The final INCM is reconstructed by using the principal eigenvector and definition of INCM. The beamformer's weight coefficients are computed by the linearly constrained minimum variance (LCMV) formulation. The proposed method is validated by experiments using a circular microphone array mounted on a tour robot in an exhibition hall. The results show that the proposed beamformer improves the robustness of automatic speech recognition and the performance of robot audition.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".