Translating the Sound of Music: Forensic Musicology and Visual Evidence in Music Copyright Infringement Cases
Bibliographic record
Abstract
Abstract In music copyright infringement cases, forensic musicologists are often called to testify as to whether or not two songs are ‘substantially similar.’ While it is standard practice to rely on experts to dissect the works in question, this is a fairly recent phenomenon. Until the 1950s, it was not the scientific analysis of the pieces, but the impressions they left on the ‘untrained ears’ of everyday listeners that was used to determine copyright infringement. This paper presents an overview of American music copyright infringement cases to document this shift in how the question of substantial similarity has been approached. We argue that the courts’ inability to objectify what listeners hear created the need for experts who could translate music into legal evidence that could be visually witnessed. This practice of judging plagiarism according to how songs look on paper may account for why the courts have viewed musical sampling as copyright violations.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".