The story of making a music generation neural network

I made a music generation neural network using my own MIDI parser and LSTM. Recently, there is a project like Magenta, and it seems to be the nth brew, but when I modified the sampling method from the output probability distribution, a little interesting result was obtained. I got it, so I'll share it.

Demo (* Note the volume for immediate playback) Repository

LSTM It is a kind of neural network that can learn the context of sequential data. For details, I will leave it to the many good articles. The following is detailed.

-Unreasonable effectiveness of recurrent neural networks (translation) --An article by Andrej Karpathy, who recently became the director of Tesla's AI and Autopilot Vision. There are many examples and it is easy to understand. -Overview of LSTM network --Details on the calculation process.

MIDI(SMF) This format expresses performance data as a sequence of chunks with pitch, volume, time information, etc. Use (part of) this sequence as LSTM input.

Process flow

  1. Parse the MIDI file to be learned and convert chunks to strings
  2. Train the model using the transformed chunk sequence
  3. Generate text data that expresses MIDI data based on the created model and a sequence of appropriate chunks. ** Randomness is added by changing the parameters used for sampling from the probability distribution each time the output is from the model. ** **
  4. Convert the output result to MIDI

result

When we tried to output elements with a low probability of occurrence at a certain frequency, a song with a slightly arranged original song was generated.

Remarks

The self-made MIDI parser is currently quite simple, and there are chunks and information that have not been processed, so if you make a little more, the quality of the output will improve.

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