Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library
Understanding students’ acoustic evaluation in learning environments is crucial for identifying acoustic issues, improving acoustic conditions, and enhancing academic performance. However, predictive models are not specifically tailored to predict students’ acoustic evaluations, particularly in educ...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-07-01
|
| Series: | Acoustics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-599X/6/3/37 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850258833849450496 |
|---|---|
| author | Dadi Zhang Kwok-Wai Mui Massimiliano Masullo Ling-Tim Wong |
| author_facet | Dadi Zhang Kwok-Wai Mui Massimiliano Masullo Ling-Tim Wong |
| author_sort | Dadi Zhang |
| collection | DOAJ |
| description | Understanding students’ acoustic evaluation in learning environments is crucial for identifying acoustic issues, improving acoustic conditions, and enhancing academic performance. However, predictive models are not specifically tailored to predict students’ acoustic evaluations, particularly in educational settings. To bridge this gap, the present study conducted a field investigation in a university library, including a measurement and questionnaire survey. Using the collected personal information, room-related parameters, and sound pressure levels as input, six machine learning models (Support Vector Machine–Radial Basis Function (SVM (RBF)), Support Vector Machine–Sigmoid (SVM (Sigmoid)), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB)) were trained to predict students’ acoustic acceptance/satisfaction. The performance of these models was evaluated using five metrics, allowing for a comparative analysis. The results revealed that the models better predicted acoustic acceptance than acoustic satisfaction. Notably, the RF and GBM models exhibited the highest performance, with accuracies of 0.87 and 0.84, respectively, in predicting acoustic acceptance. Conversely, the SVM models performed poorly and were not recommended for acoustic quality prediction. The findings of this study demonstrated the feasibility of employing machine learning models to predict occupants’ acoustic evaluations, thereby providing valuable insights for future acoustic assessments. |
| format | Article |
| id | doaj-art-79682576cb504ae0a7355ba6cc45e571 |
| institution | OA Journals |
| issn | 2624-599X |
| language | English |
| publishDate | 2024-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Acoustics |
| spelling | doaj-art-79682576cb504ae0a7355ba6cc45e5712025-08-20T01:56:01ZengMDPI AGAcoustics2624-599X2024-07-016368169710.3390/acoustics6030037Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University LibraryDadi Zhang0Kwok-Wai Mui1Massimiliano Masullo2Ling-Tim Wong3Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Architecture and Industrial Design, Università degli Studi della Campania “Luigi Vanvitelli”, 81031 Aversa, ItalyDepartment of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, ChinaUnderstanding students’ acoustic evaluation in learning environments is crucial for identifying acoustic issues, improving acoustic conditions, and enhancing academic performance. However, predictive models are not specifically tailored to predict students’ acoustic evaluations, particularly in educational settings. To bridge this gap, the present study conducted a field investigation in a university library, including a measurement and questionnaire survey. Using the collected personal information, room-related parameters, and sound pressure levels as input, six machine learning models (Support Vector Machine–Radial Basis Function (SVM (RBF)), Support Vector Machine–Sigmoid (SVM (Sigmoid)), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB)) were trained to predict students’ acoustic acceptance/satisfaction. The performance of these models was evaluated using five metrics, allowing for a comparative analysis. The results revealed that the models better predicted acoustic acceptance than acoustic satisfaction. Notably, the RF and GBM models exhibited the highest performance, with accuracies of 0.87 and 0.84, respectively, in predicting acoustic acceptance. Conversely, the SVM models performed poorly and were not recommended for acoustic quality prediction. The findings of this study demonstrated the feasibility of employing machine learning models to predict occupants’ acoustic evaluations, thereby providing valuable insights for future acoustic assessments.https://www.mdpi.com/2624-599X/6/3/37acoustic evaluationmachine learningprediction modelleaning environmentfield investigationon-site measurement |
| spellingShingle | Dadi Zhang Kwok-Wai Mui Massimiliano Masullo Ling-Tim Wong Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library Acoustics acoustic evaluation machine learning prediction model leaning environment field investigation on-site measurement |
| title | Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library |
| title_full | Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library |
| title_fullStr | Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library |
| title_full_unstemmed | Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library |
| title_short | Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library |
| title_sort | application of machine learning techniques for predicting students acoustic evaluation in a university library |
| topic | acoustic evaluation machine learning prediction model leaning environment field investigation on-site measurement |
| url | https://www.mdpi.com/2624-599X/6/3/37 |
| work_keys_str_mv | AT dadizhang applicationofmachinelearningtechniquesforpredictingstudentsacousticevaluationinauniversitylibrary AT kwokwaimui applicationofmachinelearningtechniquesforpredictingstudentsacousticevaluationinauniversitylibrary AT massimilianomasullo applicationofmachinelearningtechniquesforpredictingstudentsacousticevaluationinauniversitylibrary AT lingtimwong applicationofmachinelearningtechniquesforpredictingstudentsacousticevaluationinauniversitylibrary |