A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Decoding Cultural Music Classification with Machine Learning and Segment Length Analysis
Tekijät: Abebe, Mesfin; Endashew, Leta; Heikkonen, Jukka; Kanth, Rajeev; Mohapatra, Sudhir Kumar
Julkaisuvuosi: 2026
Lehti: SN Computer Science
Artikkelin numero: 146
Vuosikerta: 7
Numero: 2
ISSN: 2662-995X
eISSN: 2661-8907
DOI: https://doi.org/10.1007/s42979-026-04747-6
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Osittain avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1007/s42979-026-04747-6
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/509007786
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
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 (p < 0.05). 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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Open Access funding provided by University of Turku (including Turku University Central Hospital). No specific funding received.
