Smartphone-Assessed Movement Predicts Music Properties: Towards Integrating Embodied Music Cognition into Music Recommender Services via Accelerometer

Melanie Irrgang, Jochen Steffens, Hauke Wolfgang Egermann

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

Abstract

Numerous studies have shown a close relationship between move- ment and music [7], [17], [11], [14], [16], [3], [8]. That is why Leman calls for new mediation technologies to query music in a corporeal way [9]. Thus, the goal of the presented study was to explore how movement captured by smartphone accelerometer data can be re- lated to musical properties. Participants (N = 23, mean age = 34.6 yrs, SD = 13.7 yrs, 13 females, 10 males) moved a smartphone to 15 musical stimuli of 20s length presented in random order. Mo- tion features related to tempo, smoothness, size, regularity, and direction were extracted from accelerometer data to predict the musical qualities “rhythmicity", “pitch level + range" and "complex- ity“ assessed by three music experts. Motion features selected by a 20-fold lasso predicted the musical properties to the following degrees “rhythmicity" (R2 : .47), pitch level and range (R2 : .03) and complexity (R2 : .10). As a consequence, we conclude that music properties can be predicted from the movement it evoked, and that an embodied approach to Music Information Retrieval is feasible.
Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Movement and Computing, MOCO 2018
PublisherACM Digital Library
Number of pages4
ISBN (Electronic)9781450365048
DOIs
Publication statusPublished - 28 Jun 2018

Publication series

NameACM Proceedings
PublisherACM
ISSN (Electronic)2168-4081

Bibliographical note

© 2018, Author(s). This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

Keywords

  • Accelerometer
  • Embodied cognition and movement
  • Music information retrieval
  • Smartphone

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