Design of Speech Enhancement System Based on Microphone Array
Bibliographic record
Abstract
Beamforming technology plays a critical role in speech enhancement by isolating target speech from noise. This study firstly evaluates two classical and modern beamforming methods, namely Delay-Sum, CGMM-MVDR, and diverse microphone array geometries through simulated experiments, analyzing their capabilities in speech separation and intelligibility improvement. Key performance factors are examined through beampattern and spectral analysis. Accordingly, a novel post-processing mask optimization strategy is introduced to solve the critical deficiency in the time-frequency mask estimation of the CGMM-MVDR method, which yields significant performance gains. A prototype is developed that integrates a compact microphone array and embedded processing units to implement the enhanced CGMM-MVDR algorithm on portable devices. An Android application featuring gesture-based beam steering control is also designed and preliminarily validated in real-world scenarios. To improve device portability, we adopted the MQTT protocol to facilitate inter-device communication. This work bridges algorithmic improvements with practical implementation, offering insight into robust beamformer design and deployment for speech enhancement applications.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".