MMT-Net: A Lightweight Hybrid of Multi-Scale Modern TCN and Transformer for Real-Time SELD
Bibliographic record
Abstract
Existing autonomous robots rely on LiDAR and cameras to perceive their surroundings, but they remain vulnerable to occluded threats such as obstacles behind corners. Therefore, auditory perception is essential for safe operation in complex environments. Prior sound event localization and detection (SELD) approaches have employed FirstOrder Ambisonics with four microphones (FOA) with Transformer or RNN-based models. However, these architectures require large parameter counts and incur high latency, making them unsuitable for real-time operation. In this paper, we propose MMT-Net, a lightweight SELD model that uses only two microphones and integrates a Transformer-based front-end with a Multi-scale Modern Temporal Convolutional Network (MM-TCN) back-end. Compared to Conformerbased models, MMT-Net reduces the parameters count by$\approx 7 \times$(to$\sim 2 \mathrm{M}$) and achieves$\approx 3 \times$faster inference speed with only a 1 % drop in F1-score. Furthermore, on NVIDIA Jetson Xavier NX/Orin AGX, MMT-Net runs at over 20 fps, demonstrating real-time capability on embedded platforms.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".