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14th workshop gRFIA

2nd International Workshop on Reading Music Systems


Delft, November 2

12th international workshop on Machine Learning and Music


Würzburg (Germany): September 16, 2019



  1. Valero-Mas, J.J.; Benetos, E.; Iņesta, J.M.
    "A supervised classification approach for note tracking in polyphonic piano transcription"
    Journal of New Music Research, vol. 47, pp. 249--263 (2018)
    : bibtex : RUA

    In the field of Automatic Music Transcription, note tracking systems constitute a key process in the overall success of the task as they compute the expected note-level abstraction out of a frame-based pitch activation representation. Despite its relevance, note tracking is most commonly performed using a set of hand-crafted rules adjusted in a manual fashion for the data at issue. In this regard, the present work introduces an approach based on machine learning, and more precisely supervised classification, that aims at automatically inferring such policies for the case of piano music. The idea is to segment each pitch band of a frame-based pitch activation into single instances which are subsequently classified as active or non-active note events. Results using a comprehensive set of supervised classification strategies on the MAPS piano data-set report its competitiveness against other commonly considered strategies for note tracking as well as an improvement of more than +10% in terms of F-measure when compared to the baseline considered for both frame-level and note-level evaluations.

@article {
 author = "Valero-Mas, J.J.; Benetos, E.; Iņesta, J.M.",
 title  = "A supervised classification approach for note tracking in polyphonic piano transcription",
 issn = "0929-8215",
 journal = "Journal of New Music Research",
 number = "3",
 pages = "249--263",
 volume = "47",
 year = "2018"
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