A Content Analysis of the Research Approaches in Music Genre Recognition
Yazarlar (2)
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Beyza Esin Özseven
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/HORA55278.2022.9799935 | ||
| Kongre Adı | 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications | ||
| Kongre Tarihi | 09-06-2022 / 11-06-2022 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Ankara |
| Bildiri Linki | http://dx.doi.org/10.1109/hora55278.2022.9799935 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| Music is part of many areas throughout daily life, from entertainment to rehabilitation. Technological developments are widely used in music, as they are in every field. In this context, very large music databases exist both online and offline. The importance of categorizing these databases and classifying them by genre has increased. Manual categorization of music databases has a high margin of error and takes a long time. Although various tools are used for this purpose today, developments in machine learning algorithms have led researchers to work in this field. The present study is a content analysis of the studies conducted in recent years to determine music genres. Features, datasets, and machine learning methods used for recognizing music genres are reported in detail. The main purposes of this study are to describe the features of the databases used and to create an abstract on the effectiveness of the … |
| Anahtar Kelimeler |
| acoustic measurements | machine learning | MGR | music genre recognition | music information retrieval |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 2 |
| Google Scholar | 5 |