Music is known to be time series data, where the increase in the data size pose a significant challenge to build a robust music genre classification system. The robust system requires large amount of labelled music data and necessitates the requirement of capturing effective data features for enhanced classification of music genre. The proposed research focused on developing a Deep Learning (DL) framework for classification with four steps. Initially, the music labelled data is collected from the GTZAN and ballroom dataset. The collected data is pre-processed using normalization for equalizing...
