eerstar / music_transcription_maps

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

The "Learning the Attack, Decay and Pitch for Piano Music Transcription" code will be pushed soon.

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.

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.