Imbalance Data and Transfer Learning in Music Genre Classification

This project investigates the performance and effects of varying degrees of data imbalanceand the role of transfer learning in music genre classification (MGC), using a relativelysmall, imbalanced music dataset labeled by domain experts. We refine the prominent deeplearning model Audio Spectrogram Transformer (AST), under these constraints. Our re-sults show that the refined model performs exceptionally well in the task of MGC, outper-forming results of previous studies. Notably, we observe that different levels of class im-balance have minimal effect on performance, contrasting previous research based on non-transformer models, which reports improvements of minority class predictions throughbalancing techniques. These findings suggest that refining a pre-trained AST model onsmall and imbalanced datasets, can still yield prominent results. Furthermore, we sug-gest that there exists indications that the AST model may be suitable as a foundationalmodel for a broader range of music and audio related tasks, both in research and practicalapplications.