Autocorrelation of Pitch-Event Vectors in Meter Finding

  • Christopher Wm. WhiteEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11502)


Computational researchers often use autocorrelation techniques to identify the meter of a musical passage, tracking the ebs and flows of loudness or –if using symbolic data– peaks and valleys of note attacks. This paper investigates the relative success of various harmonic and pitch events compared to a note-attack model when identifying musical meter using autocorrelation. This study implements such a process using several different parameters: note attacks, pitch class change, set class probabilities, and scale-degree set probabilities. These outputs are measured against a ground truth derived from each piece’s notated time signature. The relative success of each parameter is tracked using F scores. While the study shows that loudness-oriented parameters are overall more successful, the paper discusses how its findings add to our understanding of musical meter and the role played by pitch parameters in metric accents.


Computation Corpus analysis Modeling Meter 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The University of Massachusetts AmherstAmherstUSA

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