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Automatic music transcription (AMT) of polyphonic piano using deep neural network (Implemented using pytorch).

News! Users are suggested to use our latest high-resolution piano transcription system: https://github.com/bytedance/piano_transcription

Author: Qiuqiang Kong ([email protected])

Summary

A fully connected neural network is used for training followed [1] (implemented using pytorch). Log Mel frequency with 229 bins are used as input feature [2].

Result

On the test set, frame wise F value around 75% can obtained after a few minutes training.

Dataset

Download dataset from http://www.tsi.telecom-paristech.fr/aao/en/2010/07/08/maps-database-a-piano-database-for-multipitch-estimation-and-automatic-transcription-of-music/

If you fail to download the dataset, you may download the already calculated log Mel feature & ground truth note from here https://drive.google.com/open?id=1OtK4tSrparkYVg_IrQvSDPJRtlwaQ_1k

Install requirements

  1. pip install -r requirements.txt
  2. Install pytorch following http://pytorch.org/

Run

  1. Modify dataset path in runme.sh
  2. Run ./runme.sh

Transcription result of Chopin Op. 66 Fantasie Impromptu

Real play: real_play.wav

Transcripted result: midi_result.wav

Visualization of piano roll.

alt text

Reference

[1] Sigtia, S., Benetos, E. and Dixon, S., 2016. An end-to-end neural network for polyphonic piano music transcription. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 24(5), pp.927-939.

[2] Hawthorne, C., Elsen, E., Song, J., Roberts, A., Simon, I., Raffel, C., Engel, J., Oore, S. and Eck, D., 2017. Onsets and Frames: Dual-Objective Piano Transcription. arXiv preprint arXiv:1710.11153.