The Role of Accent and Grouping Structures in Estimating Musical Meter

Han Ying Lin, Chien Chieh Huang, Wen Whei Chang*, Jen Tzung Chien

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This study presents a new method to exploit both accent and grouping structures of music in meter estimation. The system starts by extracting autocorrelation-based features that characterize accent periodicities. Based on the local boundary detection model, we construct grouping features that serve as additional cues for inferring meter. After the feature extraction, a multi-layer cascaded classifier based on neural network is incorporated to derive the most likely meter of input melody. Experiments on 7351 folk melodies in MIDI files indicate that the proposed system achieves an accuracy of 95.76% for classification into nine categories of meters.

Original languageEnglish
Pages (from-to)649-656
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number4
StatePublished - 1 Apr 2020


  • Accent periodicities
  • Grouping structure
  • Local boundary detection model
  • Meter estimation
  • Neural network

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