Comparing Pitch Spelling Algorithms on a Large Corpus of Tonal Music

  • David Meredith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3310)


This paper focuses on the problem of constructing a reliable pitch spelling algorithm—that is, an algorithm that computes the correct pitch names (e.g., C\(\sharp\)4, B\(\flat\)5 etc.) of the notes in a passage of tonal music, when given only the onset-time, MIDI note number and possibly the duration of each note. The author’s ps13 algorithm and the pitch spelling algorithms of Cambouropoulos, Temperley and Longuet-Higgins were run on a corpus of tonal music containing 1.73 million notes. ps13 spelt significantly more of the notes in this corpus correctly than the other algorithms (99.33% correct). However, Temperley’s algorithm spelt significantly more intervals between consecutive notes correctly than the other algorithms (99.45% correct). All the algorithms performed less well on classical music than baroque music. However, ps13 performed more consistently across the various composers and styles than the other algorithms.


Large Corpus Classical Music Test Corpus Pitch Interval Tonal Music 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • David Meredith
    • 1
  1. 1.Centre for Computational CreativityCity University, LondonLondonUnited Kingdom

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