Smoothed Frame-Level SINR and Its Estimation for Sensor Selection in Distributed Acoustic Sensor Networks
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Bibliographic record
Abstract
Distributed acoustic sensor network (DASN) refers to a sound acquisition system that consists of a collection of microphones randomly distributed across a wide acoustic area. Theory and methods for DASN are gaining increasing attention as the associated technologies can be used in a broad range of applications to solve challenging problems. However, unlike traditional microphone arrays or centralized systems, properly exploiting the redundancy among different channels in DASN is facing many challenges including but not limited to variations in pre-amplification gains, clocks, sensors' response, and signal-to-interference-plus-noise ratios (SINRs). Selecting appropriate sensors relevant to the task at hand is therefore crucial in DASN. In this work, we propose a speaker-dependent smoothed frame-level SINR estimation method for sensor selection in multi-speaker scenarios, specifically addressing source movement within DASN. Additionally, we devise an approach for similarity measurement to generate dynamic speaker embeddings resilient to variations in reference speech levels. Furthermore, we introduce a novel loss function that integrates classification and ordinal regression within a unified framework. Extensive simulations are performed and the results demonstrate the efficacy of the proposed method in accurately estimating smoothed frame-level SINR dynamically, yielding state-of-the-art performance.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 it