Paper dossier

MTD: A Multimodal Dataset of Musical Themes for MIR Research

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Paper year

2020

Citations

9

Authors

4

Topic labels

3

Source readout

Source and corpus status

Venue

Transactions of the International Society for Music Information Retrieval

Source slug

tismir

Corpus placement

Core corpus

Similarity rows

6

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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced

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Run label

shadow-generalization-product-candidate-ranking-v1

Snapshot

source-snapshot-shadow-generalization-v1-20260521

Scope: family global | run rank-83787b91ef

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Abstract

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.

Authors

  • Frank Zalkow
  • Stefan Balke
  • Vlora Arifi-Müller
  • Meinard Müller

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Topics

3 labels

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Music and Audio ProcessingDiverse Musicological StudiesMusic Technology and Sound Studies

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6 total neighborsEmbedding v1-title-abstract-1536-cleantext-r3

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