Bibliographic record
Abstract
This distribution includes details of the SLAC multimodal music dataset as well as features extracted from it. This dataset is intended to facilitate research comparing relative musical influences of four different musical modalities: symbolic, lyrical, audio and cultural. SLAC was assembled by independently collecting, for each of its component musical pieces, a symbolic MIDI encoding, a lyrical text transcription, an audio MP3 recording and cultural information mined from the internet. It is important to emphasize the independence of how each of these components were collected; for example, the MIDI and MP3 encodings of each piece were collected entirely separately, and neither was generated from the other. Features have been extracted from each of the musical pieces in SLAC using the jMIR (http://jmir.sourceforge.net) feature extractor corresponding to each of the modalities: jSymbolic for symbolic, jLyrics for lyrics, jAudio for audio and jWebMiner2 for mining cultural data from search engines and Last.fm (https://www.last.fm). SLAC is quite small, consisting of only 250 pieces. This is due to the difficulty of finding matching information in all four modalities independently. Although this limited size does pose certain limitations, the dataset is nonetheless the largest (and only) known dataset including all four independently collected modalities. The dataset is divided into ten genres, with 25 pieces belonging to each genre: Modern Blues, Traditional Blues, Baroque, Romantic, Bop, Swing, Hardcore Rap, Pop Rap, Alternative Rock and Metal. These can be collapsed into a 5-genre taxonomy, with 50 pieces per genre: Blues, Classical, Jazz, Rap and Rock. This facilitates experiments with both coarser and finer classes. SLAC was published at the ISMIR 2010 conference, and was itself an expansion of the SAC dataset (published at the ISMIR 2008 conference), which is identical except that it excludes the lyrics and lyrical features found in SLAC. Both ISMIR papers are included in this distribution. Due to copyright limitations, this distribution does not include the actual music or lyrics of the pieces comprising SLAC. It does, however, include details of the contents of the dataset as well as features extracted from each of its modalities using the jMIR software. These include the original features extracted for the 2010 ISMIR paper, as well as an updated set of symbolic features extracted in 2021 using the newer jSymbolic 2.2 feature extractor (published at ISMIR 2018). These jSymbolic 2.2 features include both the full MIDI feature set and a “conservative” feature set meant to limit potential biases due to encoding practice. Feature values are distributed as CSV files, Weka ARFF (https://www.cs.waikato.ac.nz/ml/weka/) files and ACE XML (http://jmir.sourceforge.net) files.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.127 |
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".