Study on evaluation model of soundscape in urban park based on Radial Basis Function Neural Network: A case study of Shiba Park and Kamogawa Park, Japan
Bibliographic record
Abstract
Abstract In order to explore whether artificial neural network can simulate and predict the subjective and objective data of soundscape in urban environment, this study developed the subjective and objective transformation model of soundscape in urban park based on radial basis function neural network. The test was conducted based on the soundscape survey data of Shiba Park and Kamogawa Park in Japan. The results showed that the subjective and objective evaluation model of soundscape constructed by radial basis function neural network could predict more accurate subjective evaluation value, the average prediction accuracy rate was 91.23%. In addition, the soundscape in the higher loudness, loudness level and sound pressure level, and the lower sharpness got a higher accuracy, which is beneficial to simulating the tourists’ psychological state of the soundscape. The study proved that the artificial neural network model can provide an effective method for further and more comprehensive acoustical environment research in the future.
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.
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 | 0.002 | 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".