Bibliographic record
Abstract
Estimates put the canon of traditional Irish dance tunes at at least seven thousand compositions. Given this diversity, a common problem faced by musicians is identifying tunes from playing. This work solves this problem by proposing Content Based Music Information Retrieval (CBMIR) systems adapted to the characteristics of traditional Irish music. The systems described in this book include an MIR prototype - MATT2 (Machine Annotation of Traditional Tunes), a segmentation system - TANSEY (Turn ANnotation from SEts using SimilaritY profiles), tunepal.org (a browser hosted MIR system) and Tunepal for iPhone/iPod touch devices. Tunepal for iPhone is a QBP system that can be used in situ in traditional music sessions. In experiments, these systems provide 93% accuracy in identifying a tune from playing. These latter systems use a backend corpus of 13,290 traditional Irish, Scots, Welsh, Breton, Canadian and American Old Time tunes drawn from community sources and standard references. This book provides extensive background information on both traditional Irish Music and Music Information Retrieval Systems in addition to detailed explanations of how these popular tools work.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".