{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:56:50Z","timestamp":1781632610297,"version":"3.54.5"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005609","name":"University of Turku","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005609","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Music is a sound composed with rhythm, melody, or harmony, and it is evolving in time and with the culture of the society. The manual way of classifying and searching of the Ethiopian music is time consuming, and expensive. In this study, an Ethiopian cultural music classification models are proposed to simplify this task. A large number of Ethiopian cultural music are collected from YouTube and other online music database. This data set has 10 classes based on the music genre. Eight machine learning algorithms are employed to build the classification models: Logistic Regression, Naive Bayes, KNN, MLP, SVM, Decision Trees, Random Forest, and Adaboost algorithms. The models are optimized using manual hyperparameter tuning, randomized search, grid search, genetic algorithm (TPOT classifier), Bayesian (hyperopt), and Optuna optimization techniques. Based on the experiments, Random Forest outperforms the other algorithms with 80% accuracy. Statistical analysis using one-way ANOVA confirmed that optimization significantly improved classification performance (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05). Confusion matrix analysis revealed that certain regional styles, such as Gojam and Somali, were more prone to misclassification due to overlapping rhythmic and tonal features. The results demonstrate that machine learning can effectively classify Ethiopian cultural music and provide a foundation for developing intelligent music retrieval and recommendation systems.\n                  <\/jats:p>","DOI":"10.1007\/s42979-026-04747-6","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T10:28:55Z","timestamp":1769768935000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decoding Cultural Music Classification with Machine Learning and Segment Length Analysis"],"prefix":"10.1007","volume":"7","author":[{"given":"Mesfin","family":"Abebe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leta","family":"Endashew","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jukka","family":"Heikkonen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajeev","family":"Kanth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudhir Kumar","family":"Mohapatra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"issue":"5","key":"4747_CR1","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TSA.2002.800560","volume":"10","author":"G Tzanetakis","year":"2002","unstructured":"Tzanetakis G, Cook P. Musical genre classification of audio signals. IEEE Trans Speech Audio Process. 2002;10(5):293\u2013302. https:\/\/doi.org\/10.1109\/TSA.2002.800560.","journal-title":"IEEE Trans Speech Audio Process"},{"key":"4747_CR2","doi-asserted-by":"publisher","unstructured":"Li T, Otsuka M, Li Q. A comparative study on content-based music genre classification. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 282\u2013289) 2003. https:\/\/doi.org\/10.1145\/860435.860487","DOI":"10.1145\/860435.860487"},{"key":"4747_CR3","unstructured":"Reed J, Lee C-H. A study on music genre classification based on universal acoustic models. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006) 2006."},{"issue":"3","key":"4747_CR4","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/BF03192561","volume":"14","author":"CN Silla","year":"2008","unstructured":"Silla CN Jr., Koerich AL, Kaestner CAA. A machine learning approach to automatic music genre classification. J Braz Comput Soc. 2008;14(3):7\u201318. https:\/\/doi.org\/10.1007\/BF03192561.","journal-title":"J Braz Comput Soc"},{"key":"4747_CR5","doi-asserted-by":"publisher","unstructured":"Liu Y, Xiang Q, Wang Y, Cai L. Cultural style-based music classification of audio signals. 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Addis Ababa University 2020."},{"issue":"4","key":"4747_CR9","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1109\/TMM.2009.2017635","volume":"11","author":"C-H Lee","year":"2009","unstructured":"Lee C-H, Shih J-L, Yu K-M, Lin H-S. Automatic music genre classification based on modulation spectral analysis of spectral and cepstral features. IEEE Trans Multimedia. 2009;11(4):670\u201382. https:\/\/doi.org\/10.1109\/TMM.2009.2017635.","journal-title":"IEEE Trans Multimedia"},{"key":"4747_CR10","doi-asserted-by":"publisher","unstructured":"Choi K, Fazekas G, Sandler M, Cho K. Convolutional recurrent neural networks for music classification. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017. https:\/\/doi.org\/10.1109\/ICASSP.2017.7952585","DOI":"10.1109\/ICASSP.2017.7952585"},{"key":"4747_CR11","unstructured":"Pons J, Lidy T, Serra X. End-to-end learning for music audio tagging at scale. 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