Model and Dataset Comparison

Simpler models were trained on a variety of datasets in order to quantitatively compare the accuracy of the model relative to older, existing models. The datasets used were:

JSB Chorales: a corpus of 382 four-part chorales by J.S. Bach.

a corpus of 382 four-part chorales by J.S. Bach. MuseData: an electronic classical music library, from CCARH at Stanford.

an electronic classical music library, from CCARH at Stanford. Nottingham: a collection of 1200 folk tunes in ABC notation, consisting of a simple melody on top of chords.

a collection of 1200 folk tunes in ABC notation, consisting of a simple melody on top of chords. Piano-Midi.de: a classical piano MIDI database. (This is the most complex dataset.)

The BALSTM network had the best performance on the music prediction task. The LSTM-NADE, a non-parallel model for comparison, had the worst performance. See the full paper for a complete analysis.

JSB Chorales MuseData Nottingham Piano-Midi.de LSTM-NADE (non-parallel) 1 | 2 | 3 1 | 2 | 3 1 | 2 | 3 1 | 2 | 3 TP-LSTM-NADE 1 | 2 | 3 1 | 2 | 3 1 | 2 | 3 1 | 2 | 3 BALSTM 1 | 2 | 3 1 | 2 | 3 1 | 2 | 3 1 | 2 | 3

You can also download all of the samples in MIDI format, if you wish.

The original training data is available at http://www-etud.iro.umontreal.ca/~boulanni/icml2012.