Paper year
2025
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
2025
Citations
3
Authors
2
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).
Concert band and wind music are deeply embedded in society and play a significant role in the cultural landscape of many countries, including Germany and Austria, particularly within the amateur music scene. However, this type of music, as well as research on wind and brass instruments in general, remains largely overlooked in the field of music information retrieval (MIR). In this paper, we address this underexplored area by introducing ChoraleBricks, a framework featuring multitrack recordings of ten different chorales, each comprising four musical parts: soprano, alto, tenor, and bass. At its core, ChoraleBricks provides isolated recordings of individual parts performed by a diverse selection of wind instruments, including flute, oboe, clarinet, trumpet, saxophone, baritone horn, trombone, and tuba. These isolated recordings act as building blocks or "bricks" that can be modularly superimposed to create full mixes with varying instrumentation. In addition, ChoraleBricks provides sheet music, time‑aligned symbolic music representations, conducting videos, and reference annotations such as fundamental frequencies and note events. The framework is further enhanced by Python software tools that support parsing, mixing, annotation, and modular combination of the recorded audio material. With all multimedia and software components available as open‑source, ChoraleBricks provides a versatile framework for generating and augmenting datasets for polyphonic wind music. It supports systematic experimentation and facilitates evaluation across various research topics, including multi‑pitch estimation, note transcription, audio alignment, and music education applications.
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music and Audio ProcessingMusic Technology and Sound StudiesAnimal Vocal Communication and Behavior
Neighbor surface
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
Wagner Ring Dataset: A Complex Opera Scenario for Music Processing and Computational Musicology
0.587Next 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.