Neural Network-Assisted Joint DOA Estimation and Beamforming with First-Order Reflection Modeling
Bibliographic record
Abstract
Source signal extraction and direction-of-arrival (DOA) estimation are core tasks in microphone array signal processing. However, their performance degrades significantly in reverberant environments: DOA estimation struggles to distinguish the direct path from strong reflections, while source extraction fails to effectively exploit the spatial cues in early reflections. This paper proposes a novel formulation of the source array-manifold vector (AMV) as a linear combination of free-field AMVs corresponding to the direct path and the four first-order reflections from the surrounding walls. A neural network is developed to jointly estimate the DOA and the combination weights of these component AMVs. The resulting composite AMV is then used in a minimum variance distortionless response (MVDR) beamformer for enhanced source extraction. Additionally, we introduce a location-based training strategy that enables end-to-end learning to automatically identify the direct-path direction. Simulation results show that the proposed method significantly improves both speech quality and DOA estimation accuracy in reverberant environments.
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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 | 0.000 |
| 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 | 0.001 |
| 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".