Multimodal System 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 scene analysis (ASA) is a challenging and multifaceted task in audio signal processing that uncovers information about the nature of an audio recording. Regardless of the analysis goal, a number of audio sources are observed in any audio scene. However, this consideration is usually not explored or given considerable thought in research. This work aims to demonstrate the utility of audio source counting with a novel solution consisting of a multimodal system for ASA. Both speaker counting and sound event counting techniques use deep neural networks (DNN) to predict the number of sources. We are able to present competitive results for audio source counting by achieving prediction accuracy of 46.03% and 89.57% with a margin of error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm 1$</tex-math></inline-formula> for speaker counting, which outperforms state-of-the-art systems for similar tasks. For sound event counting we achieve 50.55% and 86.59% prediction accuracy and accuracy with a margin of error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm 1$</tex-math></inline-formula> , respectively, that establishes a clear baseline. Our system also demonstrates real-time aspects with an overall processing time of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 0.4614$</tex-math></inline-formula> s per audio recording.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 | 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