Multi-task learning for audio scene source counting and analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Audio source counting is a fundamental task of audio scene analysis related to other audio tasks such as speaker diarization and sound event detection. It is also a relatively unexplored audio task that presents a complex challenge. In particular, source counting performance is poor when the source count range is large, limiting its potential applications. This paper presents a novel approach to improve upon audio source counting through multi-task learning. We present a first of its kind empirical study on the hierarchical nature of audio source counting, introducing the coarse source counting task and a hierarchical multi-task learning framework, in order to better understand and investigate the audio source counting task through several case study scenarios. We perform multi-task learning with a ResNet architecture and demonstrate improvements to audio source counting accuracy by up to a 6% increase from the previous best result on the SARdBScene dataset. We also perform multi-task learning of audio source counting and acoustic scene classification as a step forward for robust audio scene analysis. These experimental results show improvements of up to 6% in source counting accuracy over state-of-the-art baselines, particularly in high source count scenarios. Our findings highlight that multi-task learning not only enhances accuracy, but also improves efficiency by replacing multiple task-specific models with a single robust network.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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