This repo contains the instrument streaming model presented in the paper: Yun-Ning Hung and Yi-Hsuan Yang, "MULTITASK LEARNING FOR FRAME-LEVEL INSTRUMENT RECOGNITION"
- parse_data.py contains the function to parse the Musescore dataset
- dataset statistic.xlsx contains the statistic of Musescore dataset
- Put MP3/WAV files in the "mp3" folder
- Run the 'prediction.py' with the name of the song as the first arg
python3 prediction.py ocean.mp3
Instrument, pitch and pianorolls prediction result will be stored in the output_data folder
- Run the 'output_midi.py' with the name of the pianorolls as the first arg
python3 output_midi.py ocean.npy
Reference
If you use the pianorolls to MIDI converter, please cite this paper:
- Hao-Wen Dong, Wen-Yi Hsiao, and Yi-Hsuan Yang, "Pypianoroll: Open Source Python Package for Handling Multitrack Pianoroll," in ISMIR Late-Breaking Demos Session, 2018.