Paper year
2018
Detect emerging, bridge-candidate, and undercited papers inside a curated audio-ML corpus, then expose the signals behind every recommendation.
Paper dossier
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Paper year
2018
Citations
8
Authors
3
Topic labels
3
Source readout
Transactions of the International Society for Music Information Retrieval
tismir
Core corpus
6
Ranking readout
Ranking details could not be loaded (API 503).
A corpus of 1878 recorded works of historic electronic music from 1950-1999 has been collated. This novel data set empowers chronological study of variation over time, and the answering of research questions based on associated annotated metadata, such as art music versus popular music or comparing female and male composers. We describe the challenges of building our new corpus, audio analysis over all the works in it carried out via the SuperCollider Music Information Retrieval code library, and results of tackling two example research questions. The article involves some discussion of the material, but also accompanies release of the data itself.
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music and Audio ProcessingDiverse Musicological StudiesMusic Technology and Sound Studies
Neighbor surface
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
Next handoff
01
Use Recommended to see whether this paper behaves like an emerging or undercited signal in the current ranked feed, or how it appears on the bridge preview / diagnostics view.
02
Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.
03
Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.