By the same authors

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

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Title of host publicationProceedings of the 5th International Conference on Movement and Computing, MOCO 2018
DateAccepted/In press - 9 May 2018
DatePublished (current) - 28 Jun 2018
Number of pages4
PublisherACM Digital Library
Original languageEnglish
ISBN (Electronic)9781450365048

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.

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© 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.

    Research areas

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

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