Bibliographic record
Abstract
StarNet is an audio collection of 104 classical music pieces obtained from their corresponding free MIDI files, some of which are selected from the MusicNet dataset, and the rest are collected from the Clarinet MIDI Files archive. The pieces include two instrument tracks, and each piece is performed by two instrument sets: strings-piano and clarinet-vibraphone. The dataset has been introduced in the paper "Music-STAR: a Style Translation system for Audio-based Re-instrumentation" and has been used to train models for musical source separation and multi-instrument timbre translation. The repository includes two files, starnet_part1.tar.gz and starnet_part2.tar.gz, each containing 52 pieces. Note that no preprocessing has been applied to the audio files. Six audio files are available per piece, including the mixtures from the two instrument sets and their stems. For instance, the first piece is represented by the following files: 001.0.wav: the clarinet-vibraphone mixture 001.1.wav: the clarinet track 001.2.wav: the vibraphone track 001.3.wav: the strings-piano mixture 001.4.wav: the strings track 001.5.wav: the piano track
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.134 |
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".