This repository is the implementation of "Note-level Nonchord-tone Identification with Graph Neural Networks." (Uehara and Tojo, APSIPA ASC 2025)
- Python 3.10.12
- Required packages are listed in: requirements.txt
Download files from https://github.com/yui-u/little-organ-analyses.
Place all .musicxml and .rntxt files in a directory as below.
little-organ-book-rntxt
├── BWV599_v2_analysis.rntxt
├── BWV599_v2_score.musicxml
├── BWV600_v2_analysis.rntxt
├── BWV600_v2_score.musicxml
...
Download files named analysis.txt from https://github.com/MarkGotham/When-in-Rome/tree/master/Corpus/Early_Choral/Bach%2C_Johann_Sebastian/Chorales.
Rename analysis.txt to ChoralesXXX_analysis.rntxt and place all files in a directory as below.
Chorales
├── Chorales001_analysis.rntxt
├── Chorales002_analysis.rntxt
├── Chorales003_analysis.rntxt
...
Download files named analysis.txt from https://github.com/MarkGotham/When-in-Rome/tree/master/Corpus/Keyboard_Other/Bach%2C_Johann_Sebastian/The_Well-Tempered_Clavier_I.
Rename analysis.txt to The_Well-Tempered_Clavier_IXX_analysis.rntxt and score.mxl to The_Well-Tempered_Clavier_IXX_score.mxl; place all files in a directory as below.
wir_flatten/WTC_I
├── The_Well-Tempered_Clavier_I01_analysis.rntxt
├── The_Well-Tempered_Clavier_I01_score.mxl
├── The_Well-Tempered_Clavier_I02_analysis.rntxt
├── The_Well-Tempered_Clavier_I02_score.mxl
...
python run.py \
preprocess_dataset \
--dir_output dataset \
--dataset <Path to the directory containing rntxt (and mxl or musicxml)> \
--cv_num_set 5 \
--chord_type full
python run.py \
preprocess_dataset \
--dir_output dataset \
--dataset <Path to the directory containing rntxt (and mxl or musicxml)> \
--cv_num_set 5 \
--chord_type triad+dominant
python run.py \
inference_nct \
--dir_output inference \
--dir_preprocessed_dataset dataset/little-organ-book-rntxt-cvn5-halfbeat-triad+dominant-nctrth0.5 \
--cv_set_no 4 \
--dir_model trained_models/out-little-organ/gatv2-mp4-head3-normalized/nct-checkpoint-cv4-seed123
python run.py \
train_nct \
--dir_output out-little-organ/sage-mp4-seed123 \
--num_epochs 1024 \
--preprocessed_dataset_path dataset/little-organ-book-rntxt-cvn5-halfbeat-triad+dominant-nctrth0.5 \
--device cuda:0 \
--seed 123 \
--cv_set_no 0 \
--gnn_model_type sage \
--gnn_activation_fn relu \
--num_message_passing 3 \
--metric fscore \
--fscore_beta 1.0 \
--gradient_clip 1.0 \
--learning_rate 1e-3 \
--patience -1
python run.py \
train_nct \
--dir_output out-little-organ/gatv2-mp4-head3-seed123 \
--num_epochs 1024 \
--preprocessed_dataset_path dataset/little-organ-book-rntxt-cvn5-halfbeat-triad+dominant-nctrth0.5 \
--device cuda:0 \
--seed 123 \
--cv_set_no 0 \
--gnn_model_type gatv2 \
--gnn_activation_fn relu \
--num_message_passing 4 \
--num_gat_heads 3 \
--metric fscore \
--fscore_beta 1.0 \
--gradient_clip 1.0 \
--learning_rate 1e-3 \
--patience -1
This work was in part supported by grants from JSPS KAKENHI Grant Numbers 23K20011, 25H01169, and JST ACT-X Grant Number JPMJAX24C6. We used ABCI 3.0 provided by AIST and AIST Solutions with support from ''ABCI 3.0 Development Acceleration Use''.
Please cite the following paper when using this code:
Y. Uehara and S. Tojo,
"Note-level Nonchord-tone Identification with Graph Neural Networks",
2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (Accepted).