The clustering of expressive timing within a phrase in classical piano performances by Gaussian mixture models

Shengchen Li*, Dawn A.A. Black, Mark D. Plumbley

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)


In computational musicology research, clustering is a common approach to the analysis of expression. Our research uses mathematical model selection criteria to evaluate the performance of clustered and non-clustered models applied to intra-phrase tempo variations in classical piano performances. By engaging different standardisation methods for the tempo variations and engaging different types of covariance matrices, multiple pieces of performances are used for evaluating the performance of candidate models. The results of tests suggest that the clustered models perform better than the non-clustered models and the original tempo data should be standardised by the mean of tempo within a phrase.

Original languageEnglish
Title of host publicationMusic, Mind, and Embodiment - 11th International Symposium, CMMR 2015, Revised Selected Papers
EditorsRichard Kronland-Martinet, Mitsuko Aramaki, Sølvi Ystad
PublisherSpringer Verlag
Number of pages24
ISBN (Print)9783319462813
Publication statusPublished - 2016
Externally publishedYes
Event11th International Symposium on Computer Music Multidisciplinary Research, CMMR 2015 - Plymouth, United Kingdom
Duration: 16 Jun 201519 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9617 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Symposium on Computer Music Multidisciplinary Research, CMMR 2015
Country/TerritoryUnited Kingdom


  • Classical piano performance
  • Intra-phrase tempo
  • Model analysis
  • Model selection criteri

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