Paper Detail

RWC Revisited: Towards a Community-Driven MIR Corpus

Paper ID: https://openalex.org/W71288037842026Citations: 0core

Source

Transactions of the International Society for Music Information Retrieval

Slug: tismir

Abstract

The Real World Computing (RWC) Music Database has been a cornerstone of Music Information Retrieval (MIR) research for over two decades, offering high‑quality recordings across multiple genres, including popular, classical, and jazz music. Beyond its extensive audio collection, the dataset is enriched by aligned Musical Instrument Digital Interface (MIDI) encodings and complementary annotations, including beat, structure, and chord labels, making it a valuable resource for music structure analysis, beat tracking, chord recognition, automatic transcription, and music synchronization. Originally, the RWC audio material was distributed on physical media and made available for purchase at a nominal price. A significant development, announced and initiated with this paper, is the release of the RWC dataset under a Creative Commons license, making it freely accessible for research purposes. This transition significantly enhances the dataset's usability and supports broader adoption within the MIR research community. We outline the steps taken to enable this release and share a vision for transforming RWC into a community‑driven resource that promotes open research and collaboration. With the audio recordings now hosted on Zenodo, we also discuss strategies for dataset maintenance, annotation expansion, and reproducibility through collaborative platforms such as GitHub. This shift promotes transparency and inclusivity, helping to ensure the dataset's continued relevance for cutting‑edge MIR research. We further revisit the historical significance of the RWC dataset, incorporating insights from an interview with its original creator, Masataka Goto, and provide an overview of its current applications and future potential. In summary, by embracing an open and community‑supported approach, we aim not only to renew the dataset's impact and preserve its legacy within the MIR community but also to shed light on broader best practices for open, collaborative, and sustainable research infrastructures.

Authors

  • Stefan Balke
  • Johannes Zeitler
  • Vlora Arifi-Müller
  • Brian McFee
  • Tomoyasu Nakano
  • Masataka Goto
  • Müller Meinard

Topics

Music and Audio ProcessingMusic Technology and Sound StudiesDiverse Musicological Studies

Similar papers

Next explainability step

This page now serves real metadata from Postgres. Next, attach ranking run context and per-signal contributions.