Automatic Recognition of Eventfulness and Pleasantness of Soundscape
Bibliographic record
Abstract
A soundscape is the sound environment perceived by a given listener at a given time and space. An automatic soundscape affect recognition system will be beneficial for composers, sound designers, and audio researchers. Previous work on an automatic soundscape affect recognition system has demonstrated the effectiveness of predicting valence and arousal on responses from one expert user. Thus, further validations of multi-users' data are necessary for testing the generalizability of the system. We generated a gold standard by averaging responses from people provided people agreed with each other enough. Here, we model a set of common audio features extracted from a corpus of 120 soundscape recording samples that were labeled for valence and arousal in an online study with human subjects. The contribution of this manuscript is threefold: (1) study the inter-rater agreement showing the high level agreement between participants' responses regarding valence and arousal, (2) train stepwise linear regression models with the average responses of participants for soundscape affect recognition, which obtains better results than the previous study, (3) test the correlation between the level of pleasantness and the level of eventfulness based upon the gold standard.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".