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Computer Analysis of Sentiment Interpretation in Musical Conducting
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-2926-6518
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.ORCID iD: 0000-0002-7801-7617
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2017 (English)In: Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, IEEE, 2017, p. 400-405, article id 7961769Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a unique dataset consisting of 20 recordings of the same musical piece, conducted with 4 different musical intentions in mind. The upper body and baton motion of a professional conductor was recorded, as well as the sound of each instrument in a professional string quartet following the conductor. The dataset is made available for benchmarking of motion recognition algorithms. An HMM-based emotion intent classification method is trained with subsets of the data, and classification of other subsets of the data show firstly that the motion of the baton communicates energetic intention to a high degree, secondly, that the conductor’s torso, head and other arm conveys calm intention to a high degree, and thirdly, that positive vs negative sentiments are communicated to a high degree through other channels than the body and baton motion – most probably, through facial expression and muscle tension conveyed through articulated hand and finger motion. The long-term goal of this work is to develop a computer model of the entire conductor-orchestra communication pro- cess; the studies presented here indicate that computer modeling of the conductor-orchestra communication is feasible.

Place, publisher, year, edition, pages
IEEE, 2017. p. 400-405, article id 7961769
National Category
Computer and Information Sciences
Research subject
Speech and Music Communication
Identifiers
URN: urn:nbn:se:kth:diva-208886DOI: 10.1109/FG.2017.57ISI: 000414287400054Scopus ID: 2-s2.0-85026288976ISBN: 9781509040230 (print)OAI: oai:DiVA.org:kth-208886DiVA, id: diva2:1108504
Conference
12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, Washington, United States, 30 May 2017 through 3 June 2017
Note

QC 20170616

Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2018-09-13Bibliographically approved

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Alexanderson, SimonKjellström, Hedvig

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