musicperception and cognition - danish sound · 2019. 11. 1. · sofia dahl [email protected]...
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MUSIC PERCEPTION AND COGNITION
https://nordicsmc.create.aau.dk/
SOFIA DAHLDEPT. OF ARCHITECTURE, DESIGN AND MEDIA
TECHNOLOGY
Music as an embodied experience
”The embodied viewpoint holds that bodily involvement shapes the way weperceive, feel, experience, and comprehend music.”
• Movements encoded as musical sounds• The audio (and visual) modalities communicate expression• The first-person subjective experiences attribute intentions
Sofia Dahl [email protected]
(Leman 2008, Godøy 2003)
(Leman, Nijs, Maes, Van Dyck 2017)
Demo
Turn to look at the person next at you ….while you sing and hold a note.
Sofia Dahl [email protected]
Mock example of a picture pair similar to those used
high note
low note
What a lot of our participants did
(Huron, Dahl, Johnson, 2009; Ahrendt, Bach, Dahl 2017)Sofia Dahl [email protected]
Musicians frequently display a varietymovements other than those actuallyproducing the sounds.
Sofia Dahl [email protected]
• Four musical excerpts• Performed with intentions:
– HAPPINESS– SADNESS– ANGER– FEAR
• Two performers:– Soprano saxophone– Bassoon
Communicating expressive intent, Woodwind stimuli
(Dahl & Friberg, 2007)Sofia Dahl [email protected]
Rating expressive intent and movements
Observers rated:• Emotional content
• Anger• Sadness• Happiness• Fear
• Movement cues• Amount
none - large • Speed
slow - fast• Fluency
jerky - smooth• Regularity
irregular- regular
(Dahl & Friberg 2004, 2007)Sofia Dahl [email protected]
0 1 2 3 4 5 6
0 1 2 3 4 5 6
large
fast
smooth
regularirregular
jerky
slow
none
large
fast
smooth
regular
large
fast
smooth
regular irregular
jerky
slow
none
irregular
jerky
slow
noneamount
speed
fluency
regularity
HAPPY INTENTION
mean rating
saxophone bassoon
large
fast
smooth
regular irregular
jerky
slow
none
0 1 2 3 4 5 6
amount
speed
fluency
regularity saxophonebassoon
ANGRY INTENTION
mean rating
0 1 2 3 4 5 6
amount
speed
fluency
regularity
FEARFUL INTENTION
mean rating
saxophone bassoon
amount
speed
fluency
regularity
saxophone bassoon
mean rating
SAD INTENTION
Movement ratings of woodwind performances
(Dahl & Friberg, 2007)Sofia Dahl [email protected]
Perceptually relevant cues for movement and audio
amount speed fluency regularity
Happiness large fast
Sadness small slow smooth regular
Anger large fast jerky irregular
Fear small jerky irregular
amountsound level
speedTempo
fluencyarticulation
regularitytempo var.
Happiness large
highfast
fast staccato smallSadness small
lowslow
slowsmooth
legatoregular
final ritardAnger large
highfast
fastjerky
staccatoirregular
smallFear small
low fastjerky
staccatoirregular
large
(Dahl & Friberg, 2007; Juslin, Friberg & Bresin,2002)Sofia Dahl [email protected]
Synchronization and entrainment to a beat or pulse• Not unique to our species
• …but we find it highly pleasurable
Coordinating Drumming Movements
Time (s)0 0.5 1 1.5 2 2.5 3
Vert
ical d
ispla
cem
ent (m
m)
100
200
300
400
500
600
700
800
900
1000
1100
(Godøy, Song & Dahl, 2017; Dahl, 2018)
Collaboration within NordicSMC
https://www.uio.no/ritmo/english/projects/flagship-projects/
Playing with different timing instructions
Velocity profile of left drumstick for a player playing ahead, on or behindmetronome
Sofia Dahl [email protected]
”Pushed” ”On” ”Laid Back”
Bodily responses to (rhythmic) music
• Music can be a fantastic motivator for engaging in body movement!
Sofia Dahl [email protected]
Dancing: A whole body activity in synchrony with music
Sofia Dahl [email protected]
What determines what we find comfortable to move to?
Musical tempi covers a wide range, but not all suitable for dancing
If we “bounce” to the rhythm, our body shape (morphology) could have an influence
30 participants’Preferred beat period (ms)vs.Average leg length
75 80 85 90 95 100
200
300
400
500
600
700
Average leg length (cm)
Pre
ferr
ed b
eat r
ate
(ms)
(Dahl, Huron, Brod, Altenmüller 2014)Sofia Dahl [email protected]
“Try out different tempi and select your preferred”
Rhythmic Recurrency in Dance to Music with Ambiguous Meter
How do groups ofparticipants move to music where the metric structure is ambiguous?
Sofia Dahl [email protected] (Dahl & Sioros,2018)
A B C D
0 20 40 60 80 100 120 140 160 180
0
0.1
0.2
0
0.1
0.2
12345
participant
1234
participantReccurence�Rate
group�1group�2
Time�(sec)
melody melody melodyhighly synchopated
(Dahl & Sioros,2018)Sofia Dahl [email protected]
https://nordicsmc.create.aau.dk/
THANK YOU!
References• Ahrendt, P., Bach, C. C., & Dahl, S. (2017). Does Singing a Low-Pitch Tone Make You Look Angrier? Proceedings of the
14th Sound and Music Computing Conference 2017, 181–187. Aalto University.• Dahl, S., & Friberg, A. (2004). Expressiveness of musician’s body movements in performances on marimba. In A. Camurri &
G. Volpe (Eds.), Gesture-based Communication in Human-Computer Interaction; Lecture Notes in Artificial Intelligence (Vol. 2915, pp. 479–486). Berlin, Heidelberg: Springer Verlag.
• Dahl, S., & Friberg, A. (2007). Visual perception of expressiveness in musicians’ body movements. Music Perception: An Interdisciplinary Journal, 24(5), 433–454.
• Dahl, S., Brod, G., & Altenmüller, E. (2014). Preferred Dance Tempo: Does Sex or Body Morphology influence how wegroove? Journal of New Music Research, 43(2), 214–223.
• Dahl, S., & Sioros, G. (2018). Rhythmic Recurrency in Dance to Music with Ambiguous Meter. Proceedings of the 5th International Conference on Movement and Computing, 38:1–38:6. https://doi.org/10.1145/3212721.3212885
• Dahl, S. (2018). Movements, timing and precision of drummers. In B. Müller, S. Wolf, G.-P. Brueggemann, Z. Deng, A. McIntosh, F. Miller, & W. Scott Selbie (Eds.), Handbook of Human Motion. Springer.
• Godøy, R. I. (2003). Motor-mimetic music cognition. Leonardo, 36(4), 317–319.• Godøy, R. I., Song, M., & Dahl, S. (2017). Exploring sound-motion textures in drum set performance. Proceedings of the
Sound and Music Computing Conference, 145–152.• Huron, D., Dahl, S., & Johnson, R. (2009). Facial Expression and Vocal Pitch Height: Evidence of an Intermodal Association.
Empirical Musicology Review, 4(3), 93–100.• Juslin, P. N., Friberg, A., & Bresin, R. (2002). Toward a computational model of expression in performance: The GERM
model. Musicae Scientiae, (Special issue 2001-2002), 63–122.• Leman, M. (2008). Embodied music cognition and mediation technology. Mit Press.• Leman, M., Maes, P.-J., Nijs, L., & Van Dyck, E. (2018). What is embodied music cognition? In Springer handbook of
systematic musicology (pp. 747–760). Springer.