  Analysing music in the field of machine learning is a very difficult problem
with numerous constraints to consider. The nature of audio data, with its very
high dimensionality and widely varying scales of structure, is one of the
primary reasons why it is so difficult to model. There are many applications of
machine learning in music, like the classifying the mood of a piece of music,
conditional music generation, or popularity prediction. The goal for this
project was to develop a genre-conditional generative model of music based on
Mel spectrograms and evaluate its performance by comparing it to existing
generative music models that use note-based representations. We initially
implemented an autoregressive, RNN-based generative model called MelNet .
However, due to its slow speed and low fidelity output, we decided to create a
new, fully convolutional architecture that is based on the MelGAN [4] and
conditional GAN architectures, called cMelGAN.

Sound;Artificial Intelligence;Machine Learning;Audio and Speech Processing