Makine öğrenmesi ile müzik enstrüman sınıflandırması için bir çoklu-öznitelik yaklaşımı

Abstract

Bu çalışma, RMS Energy, Zero Crossing Rate (ZCR) ve Spectral Centroid gibi spektral ses özniteliklerinin, Random Forest ve XGBoost algoritmaları kullanılarak müzik aleti sınıflandırmasındaki performansını incelemektedir. Bu makine öğrenmesi algoritmaları, karmaşık sınıflandırma senaryolarında artırılmış doğruluk sağlama ve yüksek korelasyonlu enstrüman sınıfları arasında ayrım yapabilme yeteneğine sahiptir. Bu çalışmada makine öğrenmesi yaklaşımları açıklanabilir ve hesaplama açısından verimli olmaları ve derin öğrenmenin donanım veya veri kısıtlamaları nedeniyle uygulanamadığı durumlarda kullanışlı olmaları sebebiyle tercih edilmiştir. Deneysel çalışmada, ses öznitelikleri 20 enstrüman sınıfını içeren Philharmonia veri setinden elde edilmiştir. Her öznitelik seti tek başına ve ikili ile üçlü kombinasyonları da dahil olmak üzere toplam yedi farklı konfigürasyon değerlendirilmiştir. Doğruluk açısından en yüksek performans, tüm öznitelikler bir arada kullanıldığında elde edilmiş olup Random Forest için 0,91 ve XGBoost için 0,93’tür. Bu makine öğrenmesi algoritmaları, müzikteki akustik farklılıkları ayırt etmede özellikle başarılı olmuştur. Karışıklık matrisi analizi, her iki modelin gitar ve banjo gibi belirgin akustik özelliklere sahip enstrümanlarda en iyi performansı gösterdiğini ortaya koymuştur. Bulgular, birden fazla tamamlayıcı özelliğin kombinasyonunun müzik aleti sınıflandırma performansını artırdığını göstermiştir.

Keywords

• MIR
• enstrüman sınıflandırma
• makine öğrenmesi
• öznitelik çıkarımı

A multi-feature approach for musical instrument classification using machine learning

Abstract

This study examines the performance of a collection of spectral audio features, including RMS Energy, Zero Crossing Rate (ZCR), and Spectral Centroid, for musical instrument classification by using the Random Forest and XGBoost classifiers. These machine learning algorithms demonstrate enhanced precision in complex classification scenarios and improve the ability to discriminate among highly correlated instrument classes. Machine learning approaches were employed in this study due to being explainable, computationally efficient, and suitable when deep learning is not feasible under the constraints of hardware or data. As part of the experimental setup, the audio features were obtained from the Philharmonia dataset, which includes 20 instrument classes. Seven different configurations were evaluated, including each feature set individually, as well as their pairwise and triplet combinations. The highest performance in terms of accuracy was obtained when all attributes were utilized: 0.91 with Random Forest and 0.93 with XGBoost. These machine learning algorithms were particularly well adapted to distinguish acoustic differences in music. Confusion matrix analysis indicated that both models worked best for instruments with clear acoustic characteristics, such as guitar and banjo. The findings suggested that the combination of multiple complementary features improves the classification performance of musical instruments.

Keywords

• MIR
• instrument classification
• machine learning
• feature extraction

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