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New dataset provides 30,000 OMR ground truth examples for medieval chant

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new dataset of over 30,000 optical music recognition (OMR) ground truth examples for medieval chant, linking manuscript images to musical symbols. This dataset, created using Volpiano transcriptions from the Cantus Database, includes over 1.9 million tokens and covers various notational families and layouts. A Convolutional Recurrent Neural Network trained on this data achieved a 9.6% symbol error rate on test lines, significantly outperforming a previous model trained on Austrian sources. AI

IMPACT This dataset could significantly advance research in optical music recognition for historical music, enabling more accurate AI models for musicological analysis.

RANK_REASON The cluster describes a new academic paper detailing the creation of a novel dataset and the performance of a model trained on it. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset provides 30,000 OMR ground truth examples for medieval chant

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The cluster describes a new academic paper detailing the creation of a novel dataset and the performance of a model trained on it. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Robert Klugseder ·

    From Volpiano to Pixels: 30,000 Lines of OMR Ground Truth for Medieval Chant

    Optical music recognition (OMR) for medieval chant lacks training data linking manuscript images to musical symbols across notational families. Using existing Volpiano transcriptions from the Cantus Database, we created 30,567 image-target pairs from ten sources in eleven exports…

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