1 research outputs found
Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification
In this work, we analyse the random projections of Mel-spectrograms as
low-level features for music genre classification. This approach was compared
to handcrafted features, features learned using an auto-encoder and features
obtained from a transfer learning setting. Tests in five different well-known,
publicly available datasets show that random projections leads to results
comparable to learned features and outperforms features obtained via transfer
learning in a shallow learning scenario. Random projections do not require
using extensive specialist knowledge and, simultaneously, requires less
computational power for training than other projection-based low-level
features. Therefore, they can be are a viable choice for usage in shallow
learning content-based music genre classification.Comment: Submitted to IEEE Signal Processing Letter