Comprehensive analysis study of techniques in different domains for Turkish music genre classification task
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Nowadays, models or algorithms are used in the analysis process as the amount of data increases. Depending on the sectors, techniques in domains such as NLP, image processing, and voice analysis can be used. In this study, analyses were applied in these domains to classify music genres on the Turkish music dataset and these domains were compared. To perform the first analysis, the voice characteristic features of the songs were extracted and the success of machine learning (ML) algorithms and the CNN model were analyzed. For the next analysis, spectrograms of the songs were extracted and Keras application models were trained with transfer learning. During these analyses, audio segmentation and feature reduction techniques were also performed on the songs to analyze them. The last analysis applied textual analysis with song lyrics to the NLP domain. After preprocessing, the vector representations of these lyrics were obtained and the success of ML algorithms and the CNN model was measured. At the same time, large language models were fine-tuned and the success of these models was analyzed. As a result of all analyses, it has been shown that the ML method with the application of audio segmentation and feature reduction for voice analysis is the most successful. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Açıklama
Anahtar Kelimeler
Image analysis, Large language model, Machine learning, Natural language processing, Transfer learning, Voice analysis
