Interactive Content-Aware Music Browsing using the Radio Drum
Bibliographic record
Abstract
Portable digital music players are becoming pervasive and the size of personal digital music collections has been steadily increasing (5-10 thousand tracks are common today). The emerging area of music information retrieval (MIR) deals with all aspects of managing, analyzing and organizing music in digital formats. The majority of work in MIR follows a search/retrieval paradigm. More recently, the importance of browsing as an interaction paradigm has been realized and several novel interfaces have been proposed. In this paper, we describe a tangible interface for content-aware browsing of music collections. The radio drum is a gestural interface based on capacitance sensors that can detect the x,y,z positions of two drum sticks in a 3D volume. We describe two possible mappings that can be used for browsing music collections without relying on metadata. The first is an explicit mapping of tempo and beat strength, and the second is a music similarity space using audio feature extraction and a self organizing map (SOM)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".