Analysis of the Effect of Spectral Feature Dimensionality on Audio Classification Performance - Fratiwi, Tria Hikmah; Yuningsih, Lilis (Primakara University, 2026)
This study examines the impact of spectral feature quantity on the classification performance of dangdut music sub-genres, namely classical dangdut, dangdut rock, and dangdut koplo. Previous studies reported relatively low classification accuracy, which is presumed to be influenced by spectral features with small numerical values and dense feature distributions. To address this issue, two feature configurations were evaluated six and five spectral features using the K-Nearest Neighbor (KNN) algorithm and a Genetic Algorithm-optimized KNN (GA- KNN). Model performance was assessed using accuracy, precision, recall, and F1-score, supported by confusion matrix analysis. The results show that the six-feature configuration consistently outperforms the five- feature configuration for both methods. GA-KNN achieved the best performance with six spectral features, yielding an accuracy of 71.53%, precision of 0.7147, recall of 0.7153, and an F1-score of 0.7140, outperforming conventional KNN, which achieved an accuracy of 62.50% and an F1-score of 0.6135. When reduced to five spectral features, performance declined for both methods; GA-KNN reached an accuracy of 66.67% with an F1-score of 0.6611, while conventional KNN dropped to 52.08% accuracy with an F1- score of 0.5121, accompanied by increased misclassification between sub-genres with similar spectral and rhythmic characteristics. These findings indicate that spectral features with small numerical values still contribute meaningful discriminative information and should be carefully evaluated before applying feature reduction in music genre classification tasks.