A Temporal–Spatial Joint High-Gain Beamforming Method in the STFT Domain Based on Kronecker Product Filters
Bibliographic record
Abstract
Superdirective beamformers are highly appealing for their superior directivity and effectiveness in suppressing diffuse noise. However, their sensitivity to sensor noise and array imperfections poses significant challenges in practice. Achieving higher robustness often necessitates a trade-off in directivity, thereby reducing their ability to suppress directional and diffuse noises. A key concern, therefore, is how to improve noise suppression while maintaining robustness. To address this, we propose in this paper a novel temporal-spatial joint high-gain beamforming method based on a Kronecker product decomposition, making use of the inter-frame correlation to improve performance. The signal model in the proposed work uses recent pairs of time frames and employs the Kronecker product of the steering vector with a frequency- and angle-dependent inter-frame correlation vector. The high-gain beamformers are formulated as Kronecker product filters, where the temporal filter is optimized to maximize the white noise gain (WNG) and the spatial filter is optimized to enhance the directivity factor (DF). With accurate estimation of the correlation vector, Kronecker product high-gain beamformers can simultaneously improve both WNG and DF. The proposed method offers flexibility and can be extended to design other types of beamformers, with a maximum WNG (MWNG) beamformer presented as an example within the same framework. This paper also explores three approaches to estimating the correlation vector: time-invariant, time-varying, and data-driven estimations. Simulation results show notable improvements in noise suppression performance across various scenarios, highlighting the practical effectiveness of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".