genre music Classification with CNN

The model used is a CNN with regularization and data augmentation to improve generalization and reduce the risk of overfitting. The training data consists of three music genres: Ambient, Pop, and Rock, with a total of 910 audio segments. Each track is divided into 10 segments, and 13 MFCC features are extracted to represent the audio characteristics as input for the classification model. This model achieved 90% accuracy in classifying music genres.