Maximum a Posteriori Estimation of Piecewise Arcs in Tempo Time-Series

Daniel Stowell*, Elaine Chew

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Citations (Scopus)
28 Downloads (Pure)

Abstract

In musical performances with expressive tempo modulation, the tempo variation can be modelled as a sequence of tempo arcs. Previous authors have used this idea to estimate series of piecewise arc segments from data. In this paper we describe a probabilistic model for a time-series process of this nature, and use this to perform inference of single- and multi-level arc processes from data. We describe an efficient Viterbi-like process for MAP inference of arcs. Our approach is score-agnostic, and together with efficient inference allows for online analysis of performances including improvisations, and can predict immediate future tempo trajectories.
Original languageEnglish
Title of host publicationFrom Sounds to Music and Emotions. CMMR 2012. Lecture Notes in Computer Science
EditorsM Aramaki, M Barthet, R Kronland-Martinet, S Ystad
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages387-399
Number of pages13
Volume7900
ISBN (Electronic)978-3-642-41248-6
ISBN (Print)978-3-642-41247-9
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7900

Keywords

  • music expressivity
  • musical prosody
  • musical expression
  • music processing
  • segmentation

Fingerprint

Dive into the research topics of 'Maximum a Posteriori Estimation of Piecewise Arcs in Tempo Time-Series'. Together they form a unique fingerprint.

Cite this