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Update 20 Apr 2016: Check out the paper on () Summary Word-RNN (LSTM) on with wordified text representations of Metallica’s drumming midi files, which came from. • Midi files of Metallica track comes from. • LSTM model comes from. • Read Midi files with. • Convert them to a text file (corpus) by my rules, which are • (Temporal) Quantisation • Simplification/Omitting some notes • ‘Word’ with binary numbers • Learn an LSTM model with the corpus and generate by prediction of words. • Words in a text file → midi according to the rules I used above. A Quick look on things (copied-and-pasted from my ) LSTM LSTM (Long Short-Term Memory) is a type of RNN.

It is known to be able to learn a sequence effectively. RNN RNN (Recurrent Neural Network) is a type of deep learning neural network. See by WildML for further understanding.

Keras is a deep learning framework based on Theano and Tensorflow. I used Theano as backend but this shouldn’t affect the output. Another quick look on things Metallica. Why Metallica? Because I found quite enough number of midi track of Metallica – more than any other artists so far. Also the drum tracks in Metallica are relatively consistent. The simplification in the preprocess would also make sense for Metallica drum tracks according to my brain.

Preprocess – How to Model a rhythm representation into a text? LSTM models are basically about time-series modelling, i.e. In, it was straightforward how to model a chord progression as a text (so that I didn’t even mentioned about ‘modelling’). I converted the midi tracks into a symbolic, 1-D data to deal with it as a text. Drum track is not a 1-D No it isn’t as you see above. This is so called a piano-roll view. Y-axis is pitch, x-axis is time, and each note represents different part of drum.

Here, blue:kick, green:snare, yellow-or-olive-or-whatever:(opened) hi-hats, and red:crash cymbals. Yes, this is a piano-roll view of the drum track of Master of Puppet – from 0:28 in this live.

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