Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison
Baby cry classification is an important topic in Machine Learning, especially in the healthcare field, as crying is the primary form of communication for infants to convey their needs or conditions. Many inexperienced parents tend to interpret baby cries in a limited way, even though each cry has un...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-03-01
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| Series: | Journal of Applied Informatics and Computing |
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| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9167 |
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| author | I Putu Yogi Prasetya Dharmawan I Made Agus Dwi Suarjaya Wayan Oger Vihikan |
| author_facet | I Putu Yogi Prasetya Dharmawan I Made Agus Dwi Suarjaya Wayan Oger Vihikan |
| author_sort | I Putu Yogi Prasetya Dharmawan |
| collection | DOAJ |
| description | Baby cry classification is an important topic in Machine Learning, especially in the healthcare field, as crying is the primary form of communication for infants to convey their needs or conditions. Many inexperienced parents tend to interpret baby cries in a limited way, even though each cry has unique characteristics that represent specific needs such as hunger, discomfort, sleepiness, flatulence, and abdominal pain. With the advancement of technology, identification of baby cries can now be done automatically through AI-based applications, but the implementation is still limited. This study compares the performance of ensemble learning methods, namely Random Forest and XGBoost, with the Whisper model in classifying baby cries. The results show that the Whisper-small model has the best performance with precision 0.9115 and recall 0.9007, followed by XGBoost with slightly degraded performance after hyperparameter optimization. Random Forest showed the lowest performance among the three models. Transformer-based models such as Whisper-small proved to be superior in capturing the complex patterns of infant cries, compared to tree-based models. These findings indicate the great potential of accurate and reliable models to help parents understand the needs of infants more effectively, thereby improving the quality of infant care. |
| format | Article |
| id | doaj-art-cb0a30c080fa4ea38bcbee39c7a58929 |
| institution | DOAJ |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-cb0a30c080fa4ea38bcbee39c7a589292025-08-20T03:13:45ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-03-019227328310.30871/jaic.v9i2.91676712Baby Cry Classification Using Ensemble Learning and Whisper Method ComparisonI Putu Yogi Prasetya Dharmawan0I Made Agus Dwi Suarjaya1Wayan Oger Vihikan2Program Studi Teknologi Informasi, Universitas UdayanaProgram Studi Teknologi Informasi, Universitas UdayanaProgram Studi Teknologi Informasi, Universitas UdayanaBaby cry classification is an important topic in Machine Learning, especially in the healthcare field, as crying is the primary form of communication for infants to convey their needs or conditions. Many inexperienced parents tend to interpret baby cries in a limited way, even though each cry has unique characteristics that represent specific needs such as hunger, discomfort, sleepiness, flatulence, and abdominal pain. With the advancement of technology, identification of baby cries can now be done automatically through AI-based applications, but the implementation is still limited. This study compares the performance of ensemble learning methods, namely Random Forest and XGBoost, with the Whisper model in classifying baby cries. The results show that the Whisper-small model has the best performance with precision 0.9115 and recall 0.9007, followed by XGBoost with slightly degraded performance after hyperparameter optimization. Random Forest showed the lowest performance among the three models. Transformer-based models such as Whisper-small proved to be superior in capturing the complex patterns of infant cries, compared to tree-based models. These findings indicate the great potential of accurate and reliable models to help parents understand the needs of infants more effectively, thereby improving the quality of infant care.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9167audio classificationbaby cry classificationensemble learningwhisper modelmachine learning |
| spellingShingle | I Putu Yogi Prasetya Dharmawan I Made Agus Dwi Suarjaya Wayan Oger Vihikan Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison Journal of Applied Informatics and Computing audio classification baby cry classification ensemble learning whisper model machine learning |
| title | Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison |
| title_full | Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison |
| title_fullStr | Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison |
| title_full_unstemmed | Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison |
| title_short | Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison |
| title_sort | baby cry classification using ensemble learning and whisper method comparison |
| topic | audio classification baby cry classification ensemble learning whisper model machine learning |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9167 |
| work_keys_str_mv | AT iputuyogiprasetyadharmawan babycryclassificationusingensemblelearningandwhispermethodcomparison AT imadeagusdwisuarjaya babycryclassificationusingensemblelearningandwhispermethodcomparison AT wayanogervihikan babycryclassificationusingensemblelearningandwhispermethodcomparison |