SMCBenchmark

The processed (already converted MIDI/csv files to npy files) but unquantized SMCBenchmark for symbolic music classification. Paper name (to cite the benchmark) Jun-You Wang and Li Su, "Improving BERT for symbolic music understanding using token denoising and pianoroll prediction," ISMIR 2025. Data format Each dataset contains two files: note list and groundtruth labels. For example, 'pm2s_train.npy' is the data list while 'pm2s_train_beatans.npy' is the groundtruth of the dataset (PM2S) for the BP task. Each data list contains a list of songs; each song contains a list of notes; each note is represented as [onset, offset, MIDI pitch]. Except BP/DbP (where onset and offset times are "pseudo" crotchet beats), all onset and offset times are represented in crotchet beats. For sequence prediction tasks (SGC/PR/ER), the groundtruth is a list of integer, each indicates the class label of a song; for note-level prediction tasks, the groundtruth is a list of label list; each label in the label list indicates the class label of a note in a song. Evaluation details In our implementation, for sequence prediction tasks, the note sequences are segmented to 512-note segments, and the accuracy is computed at the segment level (not the whole song level). This follows the implementation of MidiBERT. Note that this "512" parameter has nothing to do with the actual max length of the pretrained model (actually, the default setting of the proposed M2BERT has a max note length of 1024). For cross-validation tasks, the accuracy is computed for each fold; then, we average all folds' accuracies to obtain the final results. For other evaluation details, please see https://github.com/york135/SMCBenchmark Data names Task Data files Groundtruth files SGC tagatraum_{train, valid, test}.npy tagatraum_{train, valid, test}_genreans.npy BP pm2s_{train, valid, test}.npy pm2s_{train, valid, test}_beatans.npy DbP pm2s_{train, valid, test}.npy pm2s_{train, valid, test}_downbeatans.npy CR augnet_{train, valid, test}.npy augnet_{train, valid, test}_chordrootans.npy LK augnet_{train, valid, test}.npy augnet_{train, valid, test}_localkeyans.npy ME pop909_{train, valid, test}.npy pop909_{train, valid, test}_melodyans.npy VE pop909_{train, valid, test}.npy pop909_{train, valid, test}_velocityans.npy OTC orch_{train, valid, test}.npy orch_{train, valid, test}_textureans.npy PS pianist8_fold_{0, 1, 2, 3, 4}.npy pianist8_fold_{0, 1, 2, 3, 4}_composerans.npy ER emopia_fold_{0, 1, 2, 3, 4}.npy emopia_fold_{0, 1, 2, 3, 4}_emotionans.npy VF tnua_{train, valid, test}.npy tnua_{train, valid, test}_violin_allans.npy MNID bps_motif_fold_{0, 1, 2, 3, 4}.npy bps_motif_fold_{0, 1, 2, 3, 4}_mnidans.npy Please feels free to report any issue by sending e-mail to me ([e-mail]) !