cMelGAN: An Efficient Conditional Generative Model Based on Mel Spectrograms
This work addresses music generation for applications like genre-specific creation, but it is incremental as it builds on existing GAN architectures.
The authors tackled the problem of conditional music generation by developing cMelGAN, a fully convolutional architecture based on Mel spectrograms, which improved speed and fidelity compared to an existing autoregressive RNN model.
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.