Emotion Recognition From Different Types of Music From Different Cultures

Year 2020, Volume: 35 Issue: 3, 687 - 698, 30.09.2020

Serhat Hızlısoy , Zekeriya Tüfekci

https://doi.org/10.21605/cukurovaummfd.846661

Cited By: 1

Abstract

In this study, various machine learning methods were used to recognize emotions on databases of different types of music belonging to different cultures. In order to obtain features from the music in these databases, widely used toolboxes were preferred. Correlation-based feature selection method was applied to all the obtained features. BayesNet, Sequential Minimal Optimization, Logistic Regression and Decision Trees are used as machine learning methods. When BayesNet was applied to the remaining features after the feature selection process, %94,35 recognition accuracy rate was obtained for Turkish Emotional Music Database, %79,62 for Bi-Modal Database, and %75,45 for Soundtrack Database, and better results were achieved than other classifiers. Then, the features obtained from the toolboxes were combined and the selection process was made again. After this process, recognition rates of %95,96, %80,24 and %82,72 were obtained for these databases, respectively.

Keywords

Music, Emotion recognition, Machine learning

Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma

Abstract

Bu çalışmada, klasik makine öğrenme yöntemleri farklı kültürlere ait farklı türdeki müziklerden oluşmuş veri tabanları üzerinde duygu tanıması yapmak için kullanılmışlardır. Bu veri tabanlarında bulunan müziklerden öznitelik çıkarmak için çalışmalarda yaygın olarak kullanılan araçlar tercih edilmiştir. Çıkarılan bütün özniteliklere korelasyon tabanlı öznitelik seçme yöntemi uygulanmıştır. Makine öğrenmesi yöntemleri olarak Bayes Ağları, Sıralı Minimal Optimizasyon, Lojistik Regresyon ve Karar Ağaçları kullanılmıştır. Öznitelik seçim işlemi sonrasında kalan özniteliklere Bayes Ağları yöntemi uygulandığında, Türkçe Duygusal Müzik Veri Tabanı için %94,35, Bi-Modal Veri Tabanı için %79,62 ve Soundtrack Veri Tabanı için ise %75,45 tanıma oranı elde edilmiş ve karşılaştırılan sınıflandırıcılardan daha iyi sonuç alınmıştır. Daha sonra, araçlardan çıkarılan öznitelikler bir araya getirilmiş ve yine seçim işlemi yapılmıştır. Bu işlemden sonra ise, sırasıyla bu veritabanları için %95,96, %80,24 ve %82,72 tanıma oranları elde edilmiştir.

Keywords

Müzik, Duygu tanıma, Makine öğrenmesi

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