Datasets for the Analysis of Expressive Musical Gestures

Álvaro Sarasúa, Baptiste Caramiaux, Atau Tanaka, Miguel Ortiz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper we present two datasets of instrumental gestures performed with expressive variations: five violinists performing standard pedagogical phrases with variation in dynamics and tempo; and two pianists performing a repertoire piece with variations in tempo, dynamics and articulation. We show the utility of these datasets by highlighting the different movement qualities embedded in both datasets. In addition, for the violin dataset, we report on gesture recognition tests using two state-of-the-art realtime gesture recognizers. We believe that these resources create opportunities for further research on the understanding of complex human movements through computational methods.
Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Movement Computing
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Pages13:1-13:4
Number of pages4
ISBN (Print)978-1-4503-5209-3
DOIs
Publication statusPublished - 2017

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Keywords

  • Database
  • EMG
  • Particle Filtering
  • Motion capture
  • Machine Learning
  • Hidden Markov Models
  • Gesture recognition

Cite this

Sarasúa, Á., Caramiaux, B., Tanaka, A., & Ortiz, M. (2017). Datasets for the Analysis of Expressive Musical Gestures. In Proceedings of the 4th International Conference on Movement Computing (pp. 13:1-13:4). [13] New York, NY, USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3077981.3078032