Paper year
2020
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
2020
Citations
9
Authors
4
Topic labels
3
Source readout
Transactions of the International Society for Music Information Retrieval
tismir
Core corpus
6
Ranking readout
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
0
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Bridge
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Under-cited
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Musical themes are essential elements in Western classical music. In this paper, we present the Musical Theme Dataset (MTD), a multimodal dataset inspired by "A Dictionary of Musical Themes" by Barlow and Morgenstern from 1948. For a subset of 2067 themes of the printed book, we created several digital representations of the musical themes. Beyond graphical sheet music, we provide symbolic music encodings, audio snippets of music recordings, alignments between the symbolic and audio representations, as well as detailed metadata on the composer, work, recording, and musical characteristics of the themes. In addition to the data, we also make several parsers and web-based interfaces available to access and explore the different modalities and their relations through visualizations and sonifications. These interfaces also include computational tools, bridging the gap between the original dictionary and music information retrieval (MIR) research. The dataset is of relevance for various subfields and tasks in MIR, such as cross-modal music retrieval, music alignment, optical music recognition, music transcription, and computational musicology.
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.