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Automatic Music Transcription

Automatic music transcription is the task of converting audio recordings into symbolic music notation (e.g., scores with note pitch, onset, and duration). Formalized as a research problem by Klapuri (2008), it represents one of the most challenging tasks in music information retrieval. Transcription enables music education, composition analysis, and digital preservation. Modern systems, particularly those using deep learning for piano music (Hawthorne et al., 2019), have achieved significant progress but remain far from perfect on general polyphonic music.

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Sources

  1. Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337. DOI: 10.1076/jnmr.33.3.323.27169
  2. Poliner, G. E., & Ellis, D. P. (2007). A discriminative model for polyphonic piano transcription. IEEE Transactions on Audio, Speech, and Language Processing, 15(3), 1116-1126. DOI: 10.1109/TASL.2006.881693
  3. Hawthorne, C., Elsen, E., Song, J., Roberts, A., Simon, I., Raffel, C., ... & Engel, J. (2019). Onsets and Frames: Dual-Objective Piano Transcription. In ISMIR. link

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Referenced by

ScholarGateAutomatic Music Transcription (Automatic Music Transcription Algorithm). Retrieved 2026-06-04 from https://scholargate.app/en/music-information-retrieval/automatic-music-transcription