Multi-modal dataset for music genre recognition based on six different modalities for LMD-aligned and SLAC datasets
Bibliographic record
Abstract
Multi-modal dataset for music genre recognition based on six different modalities for the LMD-aligned [1] and SLAC [2] datasets. Further details are provided in [3]. <strong>Descriptions of files</strong> Link Description LMD-aligned_Filelist.arff File list with 1575 music tracks selected from the LMD-aligned dataset [1] with tagtraum genre annotations [4] (only a subset of LMD-aligned is used, which includes only pieces for which all six modalities were accessible, and which includes only well-represented genres) LMD-aligned_ExtractedFeatures.tar.gz Raw audio signal and model-based features extracted with AMUSE [5] LMD-aligned_ProcessedFeatures.tar.gz Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames LMD-aligned_Datasets.tar.gz Training, optimization, and test datasets for 3 splits for the recognition of 5 genres in [3] SLAC_Filelist.arff File list with 250 music tracks from the SLAC dataset [2] (genres and sub-genres are provided in the folder structure) SLAC_ExtractedFeatures.tar.gz Raw audio signal and model-based features extracted with AMUSE [5] SLAC_ProcessedFeatures.tar.gz Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames SLAC_Datasets.tar.gz Training, optimization, and test datasets for 3 splits for the recognition of 5 genres and 10 sub-genres in [3] <strong>Modalities and feature sub-groups</strong> Modality Sub-group Dimensions in processed features of LMD-aligned Dimensions in processed features of SLAC Audio signal Low-level 1-524 1-524 Audio signal Semantic 525-810 525-810 Audio signal Structural complexity 811-908 811-908 Model-based Instruments 909-1018 909-1018 Model-based Moods 1019-1146 1019-1146 Model-based Various 1147-1402 1147-1402 Playlists Genres 1403-1973 1403-1973 Playlists Styles 1974-1695 1974-1695 Symbolic Pitch 1696-1757 1696-1757 Symbolic Melodic 1758-1781 1758-1781 Symbolic Chords 1782-1836 1782-1836 Symbolic Rhythm 1837-1935 1837-1935 Symbolic Tempo 1936-1963 1936-1963 Symbolic Instrument presence 1964-2441 1964-2441 Symbolic Instruments 2442-2456 2442-2456 Symbolic Texture 2457-2480 2457-2480 Symbolic Dynamics 2481-2484 2481-2484 Album covers SIFT 2485-2584 2485-2584 Lyrics jLyrics descriptors 2585-2603 2585-2671 Lyrics Bag-of-Words 2604-2703 Lyrics Doc2Vec 2704-2803
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".