Use of Logistic Regression Models as a Supervised Learning Algorithm to Identify Impulsive Sounds in Monitored Sound Data
Bibliographic record
Abstract
Impulsive sounds, characterized by their transient nature, often pose challenges in sound monitoring applications such as environmental noise assessments, where impulsive sounds need to be identified and processed separately from other impulsive and non-impulsive sounds. Several methods exist to identify impulsive sounds, such as through listening to recorded audio or a manual examination of the logged frequency-spectral data. This paper presents of the use of logistic regression models, implemented as a supervised learning algorithm, to identify impulsive sounds from monitored sound data. Logged spectral sound pressure data from sound level meters with were used as input. A segment of the data was pre-labelled for the impulsive sounds of interest and was used to train a logistic regression model to identify the same impulsive sounds in other data. This method aims to automate some of the analysis procedure required for handling large volumes of spectral data involving impulsive sounds.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".