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

cMelGAN: An Efficient Conditional Generative Model Based on Mel Spectrograms