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Memory Sampling for Domain-Specific Music Generation

The implementation of the LSTM network was based on the work of Sigurður Skúli (link) and the VAE implementation was based on the tutorial by François Chollet on Keras (link).

Requirements

  • Python 3.6
  • Keras 2.1.6
  • Tensorflow (used as Keras's backend) 1.8.0
  • Music21 5.1.0

The two last requirements are only necessary if you want to be able to listen to MIDI files directly with Python.

  • midi2audio 0.1.1
  • FluidSynth 1.1.1 (and at least one audio font)

Dataset

In our experiments, we have used the Essen Folksong Collection (link). It is composed of more than 8 000 folksongs from all around the world. However, the collection consists mainly of songs from China and Germany (more than 7 000 songs). Therefore, we have a dataset that is very sparse and the generated songs without any constraint will be greatly influenced by those two styles.

Each song is composed of one instrument playing a short melody (the average number of notes by files is 50). The notes are going from A2 to C7 and it makes a total of 76 different notes (flat and sharp included, C# is considered different than D-).

How to reproduce?

Some of the information is still hardcoded in some files for now so some manipulations will be needed.

  • First, download the dataset on your computer and change the absolute path toward the dataset in config.py.

  • python preprocessing.py to parse the dataset and create new files containing the information we need.

  • python train.py to train the LSTM network over the whole dataset.

  • Then, you can generate memories of the network according to some constraints. The constraints are hardcoded in the file generate_memories.py, it will select each song that matches one of the conditions in their file name to create the memories. Once you have written your condition, you also need to write the name of the file containing the saved weights of the network and then run python generate_memories.py.

  • Once the memories have been generated, you can train the VAE over the memories. First, you have to put the path toward the .npy containing the memories in vae/vae_data.py. After that, you can train the VAE with python vae/vae_train.py.

  • Finally, rewrite your condition and the name of the weight file in predict_specific.py and then run it with python predict_specific.py to generate a song that matches the conditions.

Some results

You can also listen to some of the songs we have generated with the network on this YouTube playlist.

Songs 3, 4, 5 and 7 are generated by the network as classical Chinese music and songs 1, 2, 6 and 8 as classical Italian music.