Bibliographic record
Abstract
This thesis explores audio scene analysis (ASA) for determining the number of active sources in an audio scene, a task that is defined as audio source counting. A first of its kind dataset called SARdB is produced with audio and text modalities, and annotations for the number of speakers and the number of sound events present in an audio recording. For speaker counting, an audio-based ResNet-34 and text-based Bidirectional Long Short-Term Memory (BLSTM) network set a baseline prediction accuracy of 46.03% and 89.57% when considering a margin of error of one speaker, while outperforming various state-of-the-art systems in speaker counting. Another audio-based ResNet-34 model demonstrates the optimal result for sound event counting at 50.55% prediction accuracy and 86.59% accuracy with a margin of error of one sound event. The proposed method for source counting is also shown to perform in real-time with an overall processing time of ∼0.4614s.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.031 |
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".