YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations
Bibliographic record
Abstract
YM2413-MDB is an 80s FM video game music dataset with multi-label emotion annotations. It includes 669 audio and MIDI files of music from Sega and MSX PC games in the 80s using YM2413, a programmable sound generator based on FM. The collected game music is arranged with a subset of 15 monophonic instruments and one drum instrument. They were converted from binary commands of the YM2413 sound chip. Each song was labeled with 19 emotion tags by two annotators and validated by three verifiers to obtain refined tags For more detailed information about the dataset, please refer to our paper: YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations. File Description 1) Pure data - original_vgms: crawled vgm files from SMS POWER and VGMRIPs - wav: rendered vgm files using VGMPlay 2) MIDI data - midi/vgmplay_log_to_midi: converted midi files - midi/adjust_tempo: add postprocessing(metrically aligned using wav_downbeat files) after midi conversion - midi/adjust_tempo_remove_delayed_inst: add postprocessing(metrically aligned using wav_downbeat files, remove delayed instrument) after midi conversion 3) Metadata - emotion_annotation/verified_annotation.csv: contains emotion annotation for each songs - tags_kor_eng.txt: Korean <-> English tag dictionary 4) Useful middle-time step data - wav_downbeat: extracted downbeat values using TCNBeatTracker of madmom - vgm_txts: disassembled vgm files as txt using vgm2txt - ydr: YM2413 Disassembly Raw(YDR). command list of vgm files. generated by reading vgm_txts Update Log - version 1.0.1: Fix ticks per beat value adjust to tempo where tempo values are not 150. Also, madmom downbeat files are updated from DBNBeatTracker(ISMIR, 2015) to TCNBeatTracker(Newer one EUSIPCO, 2019). - version 1.0.2: Wrong emotion tag issue in the verification annotation file was fixed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.038 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".