Design of 3D Steerable Frequency-Invariant Beamformers With Concentric Circular Superarrays
Bibliographic record
Abstract
Superarrays refer to microphone arrays that combine both omnidirectional and directional sensors, enabling higher array gain or additional functionalities that traditional arrays with the same geometry cannot achieve. In our previous work, we demonstrated that concentric circular superarrays (CCSAs) can achieve directivity and three-dimensional (3D) steering capabilities comparable to volumetric arrays, but with a more compact, two-dimensional geometry. This makes CCSAs particularly promising for high-fidelity speech signal in compact devices. However, prior CCSA designs have been limited to omnidirectional and bidirectional sensors, restricting their flexibility and broader applicability. Additionally, the theoretical conditions required for effective array configuration remain unclear. This paper introduces a generalized framework for designing CCSAs and their corresponding fully steerable, frequency-invariant beamformers in 3D space. The contributions are twofold: 1) a method is proposed for designing 3D steerable, frequency-invariant beamformers using CCSAs equipped with directional microphones of arbitrary types; and 2) the necessary and sufficient conditions for array configurations are derived to enable effective design of first-, second-, and third-order frequency-invariant beamformers. We further summarize the construction of first-, second-, and third-order beamformers. Extensive simulations and real experiments validate the proposed method and demonstrate the practical efficacy and theoretical novelty of our approach.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".