A comprehensive study of classification of music genres with voice analysis

Year 2024, Volume: 13 Issue: 1, 325 - 333, 15.01.2024

Zekeriya Anıl Güven

https://doi.org/10.28948/ngumuh.1344605

Abstract

Voice analysis is a type of analysis that can be applied in many areas such as disease detection, emotion analysis, and classification of music genres. Classification can be made by applying image or data analysis according to the problem with voice characteristics. In this study, the classification of music genres was performed by extracting the voice characteristics. GTZAN, which consists of 10 music genre tags, was used as the dataset. In the analysis phase, the effects of audio segmentation and feature selection methods on the classification of music genres were investigated. Machine learning methods and deep neural networks were used for classification. In the first stage of the analysis, the accuracy value increased by 10.58% by applying only the audio segmentation. As a result of this study, after the application of audio segmentation and feature selection methods, Deep Neural Network provided an increase of 14.19% with an accuracy of 95.19%.

Keywords

Voice Analysis, Machine Learning, Deep Neural Networks, Feature Selection, Audio Segmentation

Ses analizi ile müzik türlerinin sınıflandırılmasına yönelik kapsamlı bir çalışma

Abstract

Ses analizi; hastalık tespiti, duygu analizi, müzik türleri sınıflandırma gibi birçok alanda uygulanabilen bir analiz türüdür. Ses karakteristik özellikleri ile probleme göre görüntü analizi, veri analizi uygulanarak sınıflandırma yapılabilmektedir. Bu çalışmada, ses karakteristik özellikleri çıkarılarak müzik türlerinin sınıflandırması gerçekleştirilmiştir. Veri seti olarak 10 müzik türü etiketinden oluşan GTZAN kullanılmıştır. Analiz aşamasında, ses bölütleme işlemi ve özellik seçme yöntemlerinin de müzik türünü sınıflandırmaya etkisi araştırılmıştır. Sınıflandırma için makine öğrenme yöntemleri ve derin sinir ağlarından yararlanılmıştır. Analizin ilk aşamasında, sadece ses bölütleme işlemi uygulanmasıyla doğruluk değeri %10.58 artış göstermiştir. Çalışmanın sonucunda, ses bölütleme işlemi ve özellik seçme yöntemleri uygulanması sonrasında öğrenme yöntemlerinden Derin Sinir Ağları yöntemi %95.19 doğruluk değeriyle %14.19 başarı artışı sağlamıştır.

Keywords

Ses Analizi, Makine Öğrenmesi, Derin Sinir Ağları, Özellik Seçme, Ses Bölütleme

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