music-genre-classification-using-CNN

libraries

• Tensorflow
• Matplotlib
• Librosa
• Numpy
• Pandas

dataset

• fma_small
  • 8,000 tracks of 30s, 8 balanced genres (7.2gb)

methodoloy

• Mel Spectrograms were created from the first 10 seconds of each song in the dataset
• As these spectrograms are images, we can treat this as an immage classification problem
• We train a CNN to classify these spectrograms into their genres
  

training

• An adam optimizer was used for training
• Categorical Crossentropy loss was used since it was a classification problem
• We noticed overfitting, to reduce this we
  • Removed instrumental songs from our data since it showed the highest misclassification
  • Reduced complexity of our CNN model
  • Added regularization and a dropout layer
• We got the following Accuracy and Loss graphs (blue = validation | orange = training)
  

results

• 626/994 songs were classified correctly 
• Accuracy of 62.97% was obtained
• Hip Hop and Folk songs were classified well
• The model had trouble classifying Pop and Rock songs