Direction of Arrival Estimation of Moving Sound Sources using Deep Learning
Bibliographic record
Abstract
Sound source localization is an important task for several applications and the use of deep learning for this task has recently become a popular research topic. While nearly all previous work has focused on static sound sources, in this paper we evaluate the performance of a deep learning classification system for localization of moving sound sources and we evaluate the effect of different hyperparameters and acoustic conditions. A feedforward neural network is used to estimate the direction of arrival of moving sound sources, with Short Time Fourier Transform input features. Diverse synthetic datasets are generated to represent different acoustic conditions, and hyperparameters are tested to determine which combination results in better direction-of-arrival detection for moving sources. We evaluate the performance of the different combinations in terms of precision and recall, in a multi-class multi-label classification framework, and we find that (1) the number of frequency bins and the reverberation time have a significant effect for localizing high-speed sources, and (2) precision and recall decay slowly at low speeds while dropping sharply at high speeds.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".