A Resampling Approach for Imbalanceness on Music Genre Classification using Spectrograms

In real-world problems, modeled as machine learning tasks, the datasets are typically unbalanced, meaning that some classes have much more instances than others. In the Music Information Retrieval field it is not different and songs datasets usually are very unbalanced. Considering this scenario, we propose a novel approach to face the class imbalance problem applied to music genre classification. The proposed method uses vertical sliced spectrograms extracted from the songs' audio signal to apply oversampling and undersampling into the minority and majority classes, respectively. The experimental results for F-Score measure showed that our approach was able to beat the best result of Random Undersampling technique by 0.086, using MultiLayer Perceptrons. Besides, comparing to the baseline results, our approach significantly increased the individual results for all the minority classes.