On the Interrelation Between Listener Characteristics and the Perception of Emotions in Classical Orchestra Music

This study deals with the strong relationship between emotions and music, investigating three main research questions: (RQ1) Are there differences in human music perception (e.g., emotions, tempo, instrumentation, and complexity), according to musical education, experience, demographics, and persona...

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Published inIEEE transactions on affective computing Vol. 9; no. 4; pp. 507 - 525
Main Authors Schedl, Markus, Gomez, Emilia, Trent, Erika S., Tkalcic, Marko, Eghbal-Zadeh, Hamid, Martorell, Agustin
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Abstract This study deals with the strong relationship between emotions and music, investigating three main research questions: (RQ1) Are there differences in human music perception (e.g., emotions, tempo, instrumentation, and complexity), according to musical education, experience, demographics, and personality traits?; (RQ2) Do certain perceived music characteristics correlate (e.g., tension and sadness), irrespective of a particular listener's background or personality?; (RQ3) Does human perception of music characteristics, such as emotions and tempo, correlate with descriptors extracted from music audio signals? To investigate our research questions, we conducted two user studies focusing on different groups of subjects. We used web-based surveys to collect information about demographics, listening habits, musical education, personality, and perceptual ratings with respect to perceived emotions, tempo, complexity, and instrumentation for 15 segments of Beethoven's 3 rd symphony, "Eroica". Our experiments showed that all three research questions can be affirmed, at least partly. We found strong support for RQ2 and RQ3, while RQ1 could be confirmed only for some perceptual aspects and user groups.
AbstractList This study deals with the strong relationship between emotions and music, investigating three main research questions: (RQ1) Are there differences in human music perception (e.g., emotions, tempo, instrumentation, and complexity), according to musical education, experience, demographics, and personality traits?; (RQ2) Do certain perceived music characteristics correlate (e.g., tension and sadness), irrespective of a particular listener's background or personality? (RQ3) Does human perception of music characteristics, such as emotions and tempo, correlate with descriptors extracted from music audio signals? To investigate our research questions, we conducted two user studies focusing on different groups of subjects. We used web-based surveys to collect information about demographics, listening habits, musical education, personality, and perceptual ratings with respect to perceived emotions, tempo, complexity, and instrumentation for 15 segments of Beethoven's 3rd symphony, “Eroic”. Our experiments showed that all three research questions can be affirmed, at least partly. We found strong support for RQ2 and RQ3, while RQ1 could be confirmed only for some perceptual aspects and user groups.
This study deals with the strong relationship between emotions and music, investigating three main research questions: (RQ1) Are there differences in human music perception (e.g., emotions, tempo, instrumentation, and complexity), according to musical education, experience, demographics, and personality traits?; (RQ2) Do certain perceived music characteristics correlate (e.g., tension and sadness), irrespective of a particular listener's background or personality?; (RQ3) Does human perception of music characteristics, such as emotions and tempo, correlate with descriptors extracted from music audio signals? To investigate our research questions, we conducted two user studies focusing on different groups of subjects. We used web-based surveys to collect information about demographics, listening habits, musical education, personality, and perceptual ratings with respect to perceived emotions, tempo, complexity, and instrumentation for 15 segments of Beethoven's 3 rd symphony, "Eroica". Our experiments showed that all three research questions can be affirmed, at least partly. We found strong support for RQ2 and RQ3, while RQ1 could be confirmed only for some perceptual aspects and user groups.
Author Schedl, Markus
Martorell, Agustin
Tkalcic, Marko
Eghbal-Zadeh, Hamid
Trent, Erika S.
Gomez, Emilia
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Cites_doi 10.2466/pms.2003.96.3c.1117
10.1145/2702613.2732754
10.1016/j.cub.2009.02.058
10.1111/j.1467-6494.1992.tb00970.x
10.1109/T-AFFC.2012.5
10.1348/000712606X111177
10.1093/cercor/bhr353
10.1201/b10731
10.1145/2872518.2889368
10.1109/TAFFC.2014.2343222
10.1109/ICMLA.2008.96
10.1109/PASSAT/SocialCom.2011.26
10.1109/TAFFC.2014.2330816
10.1080/09298215.2010.513733
10.1080/0929821042000317813
10.1348/000712610X506831
10.1016/j.jrp.2005.08.007
10.1007/s11257-016-9173-y
10.1109/TASL.2010.2064164
10.2307/40285811
10.1511/2001.4.344
10.1057/jt.2009.5
10.1016/S0092-6566(03)00046-1
10.1007/s11257-016-9171-0
10.1037/h0054832
10.1073/pnas.1218772110
10.1037/0022-3514.84.6.1236
10.1037/a0022406
10.1037/h0077714
10.1037/1528-3542.8.4.494
10.2307/3344092
10.2307/2529310
10.1145/2168752.2168754
10.1177/0305735607070382
10.1037/0022-3514.60.1.37
10.1177/0305735610362821
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References ref13
ref56
ref59
ref15
ref58
ref14
ref11
ref54
ref10
ref17
ref16
ref19
ref18
sloboda (ref3) 2001
kosinski (ref46) 2013; 110
eerola (ref22) 2009
schmidt (ref27) 2009
ref51
ref50
song (ref28) 2012
ref45
ref48
ref47
ekman (ref6) 1972
ref41
ref44
laurier (ref5) 2011
hu (ref33) 2010
ref7
ref9
ref4
kim (ref31) 2010
ferwerda (ref40) 2015
li (ref30) 2003
tkal?i? (ref42) 2015
laurier (ref34) 2009
krippendorff (ref57) 2013
ref37
ref36
hu (ref32) 2008
ref2
ref1
ref39
ref38
bogdanov (ref53) 2014; 6
hu (ref35) 2009
hu (ref25) 2009
skowron (ref49) 2016
juslin (ref8) 2001
ref24
ref23
ref26
ref20
john (ref43) 1999; 2
ref21
laurier (ref12) 2009
ekman (ref55) 1999
liu (ref29) 2003
zenter (ref52) 2008; 8
References_xml – ident: ref9
  doi: 10.2466/pms.2003.96.3c.1117
– year: 1972
  ident: ref6
  publication-title: Emotion in the human face Guidelines for research and an integration of findings
– ident: ref41
  doi: 10.1145/2702613.2732754
– year: 2003
  ident: ref30
  article-title: Detecting emotion in music
– year: 2009
  ident: ref27
  article-title: Projection of acoustic features to continuous valence-arousal mood labels via regression
– ident: ref19
  doi: 10.1016/j.cub.2009.02.058
– ident: ref36
  doi: 10.1111/j.1467-6494.1992.tb00970.x
– ident: ref50
  doi: 10.1109/T-AFFC.2012.5
– start-page: 364
  year: 2015
  ident: ref42
  article-title: Personality correlates for digital concert program notes
  publication-title: Proc Int Conf User Modeling Adaptation and Personalization
– ident: ref39
  doi: 10.1348/000712606X111177
– ident: ref13
  doi: 10.1093/cercor/bhr353
– ident: ref20
  doi: 10.1201/b10731
– start-page: 107
  year: 2016
  ident: ref49
  article-title: Fusing social media cues: Personality prediction from Twitter and Instagram
  publication-title: Proc 25th Int Conf Companion World Wide Web
  doi: 10.1145/2872518.2889368
– year: 2013
  ident: ref57
  publication-title: Content Analysis An Introduction to Its Methodology
– ident: ref17
  doi: 10.1109/TAFFC.2014.2343222
– ident: ref26
  doi: 10.1109/ICMLA.2008.96
– year: 2009
  ident: ref35
  article-title: Lyric-based song emotion detection with affective lexicon and fuzzy clustering method
– ident: ref47
  doi: 10.1109/PASSAT/SocialCom.2011.26
– year: 2012
  ident: ref28
  article-title: Evaluation of musical features for emotion classification
– ident: ref45
  doi: 10.1109/TAFFC.2014.2330816
– year: 2010
  ident: ref33
  article-title: When lyrics outperform audio for music mood classification: A feature analysis
– year: 2008
  ident: ref32
  article-title: The 2007 MIREX audio mood classification task: Lessons learned
– ident: ref24
  doi: 10.1080/09298215.2010.513733
– ident: ref7
  doi: 10.1080/0929821042000317813
– ident: ref4
  doi: 10.1348/000712610X506831
– ident: ref44
  doi: 10.1016/j.jrp.2005.08.007
– ident: ref51
  doi: 10.1007/s11257-016-9173-y
– year: 2009
  ident: ref25
  article-title: Lyric text mining in music mood classification
– year: 2011
  ident: ref5
  article-title: Automatic classification of music mood by content-based analysis
– ident: ref23
  doi: 10.1109/TASL.2010.2064164
– ident: ref18
  doi: 10.2307/40285811
– ident: ref56
  doi: 10.1511/2001.4.344
– start-page: 71
  year: 2001
  ident: ref8
  publication-title: Psychological Perspectives on Music and Emotion
– start-page: 9
  year: 2009
  ident: ref34
  publication-title: Automatic Detection of Emotion in Music Interaction with Emotionally Sensitive Machines
– ident: ref59
  doi: 10.1057/jt.2009.5
– ident: ref54
  doi: 10.1016/S0092-6566(03)00046-1
– volume: 6
  start-page: 855
  year: 2014
  ident: ref53
  article-title: Essentia: An open source library for audio analysis
  publication-title: ACM SIGMM Records
– ident: ref48
  doi: 10.1007/s11257-016-9171-0
– ident: ref1
  doi: 10.1037/h0054832
– volume: 110
  start-page: 5802
  year: 2013
  ident: ref46
  article-title: Private traits and attributes are predictable from digital records of human behavior
  publication-title: Proc Natl Acad Sci United States America
  doi: 10.1073/pnas.1218772110
– volume: 8
  year: 2008
  ident: ref52
  article-title: Emotions evoked by the sound of music: Characterization, classification, and measurement
  publication-title: Emotion
– year: 2001
  ident: ref3
  publication-title: Music and Emotion Theory and Research
– year: 2009
  ident: ref12
  article-title: Music mood representation from social tags
– ident: ref37
  doi: 10.1037/0022-3514.84.6.1236
– ident: ref16
  doi: 10.1037/a0022406
– ident: ref11
  doi: 10.1037/h0077714
– year: 2010
  ident: ref31
  article-title: Music emotion recognition: A state of the art review
– year: 2009
  ident: ref22
  article-title: Prediction of multidimensional emotional ratings in music from audio using multivariate regression models
– year: 2015
  ident: ref40
  article-title: Personality & emotional states: Understanding users music listening needs
  publication-title: UMAP Extended Proc
– ident: ref10
  doi: 10.1037/1528-3542.8.4.494
– ident: ref2
  doi: 10.2307/3344092
– year: 2003
  ident: ref29
  article-title: Automatic mood detection from acoustic music data
– ident: ref58
  doi: 10.2307/2529310
– ident: ref21
  doi: 10.1145/2168752.2168754
– ident: ref38
  doi: 10.1177/0305735607070382
– start-page: 45
  year: 1999
  ident: ref55
  publication-title: Basic Emotions
– ident: ref14
  doi: 10.1037/0022-3514.60.1.37
– volume: 2
  start-page: 102
  year: 1999
  ident: ref43
  article-title: The Big Five trait taxonomy: History, measurement, and theoretical perspectives
  publication-title: Handbook of Personality Theory and Research
– ident: ref15
  doi: 10.1177/0305735610362821
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Snippet This study deals with the strong relationship between emotions and music, investigating three main research questions: (RQ1) Are there differences in human...
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SubjectTerms agreement and correlation in music perception
audio analysis
Audio signals
classical music
Complexity
Complexity theory
Demographics
Education
Emotion perception in music
Emotion recognition
Emotions
Feature extraction
Instruments
Mood
Multiple signal classification
Music
Perception
Perceptions
Personality
User groups
user study
Title On the Interrelation Between Listener Characteristics and the Perception of Emotions in Classical Orchestra Music
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