Individual Differences Analysis of Affective Traits for Chord Listening-Study of TriadsTomonori Nishida

Yoichi Yamazaki

Noriko Nagata

Nagata Laboratory, Department of Human System Interaction, Graduate School of Science and Technology, Kwansei Gakuin University (KGU), Japan

Result 2 (Model construction)

IntroductionStructural equation modeling (Fig. 9)- Construction by type
- Visualization of the connection
between acoustic features and each evaluation item

 Background- Promoting design that considers individual differences
 Enhance wellbeing
 Reduce social loss Realize a sustainable society- Researchers have conducted many studies that quantify sound impressions
 Comfort with dissonance (Popescu et al., 2019) Affections associated with cadences (Smit et al., 2020)
 Individual differences in affective evaluation of chords have rarely been examined

Structural differences between types (Fig. 10)- PleasantWeak positive, strong negative: increases with increasing modality
Weak negative: increases with decreasing modality‐More than twice as many valid paths exist than other types

 Purpose

Factor Analysis- Model individual differences in affective evaluation of triads

重さ項目Methods
Type Classification
Model Construction

協和

Factor1

Factor2

Factor3

共通性

よどんだ

.866

-.100

-.112

.816

複雑な.675

.063

-.117

.440

鈍い.669.067-.120.432

澄んだ-.620

.273

-.057

.579

ものたりない

.444

.125

.169

.208

柔らかい

.162

.895

-.060

.748

溶け合う-.275

.681

-.128

.700

溶け合わない

.416

-.549

.120

.656

浮かんだ-.273

.370

.145

.282

重厚な.224

.168

-.824

.747

低音にのびのある

.122

.208

-.632

.463

弱々しい.371

.486

.599

.571

因子寄与

3.157

2.605

1.616

Experiment

Acoustic Features

9 Conducted

Strong negative

Stimulus Considerations

弱さ

Summarize in 5 Dimensions

Stimuli

AF1（シャープさ） AF2（粗さ）20 stimuli

15 stimuli
Weak negative

Hearing Analysis

Environment

Weak positive

AF3（調性Major）

AF4（安定性）

AF5（豊かさ）max_envelope
atkleap
atktimeatkslope
ratiomajratiominratiomaj_atk
ratiomin_atk
ratiomaj_dsr
ratiomin_dsr
meanLoudness
meanLoudnessA-0.050981691-0.050981691
0.068158559-0.181054491
0.022366734-0.022366734-0.0697237
0.0697237
0.045476861-0.045476861
0.040297944-0.088348221

0.251966385
0.251966385
0.192546617-0.095266886-0.012435683
0.012435683
0.038323476-0.038323476-0.032929753
0.032929753
0.213692904
0.176666257

0.000651918
0.000651918-0.003971453
0.002878872
0.41707085-0.41707085
0.388191927-0.388191927
0.412818644-0.412818644
0.016353022
0.030369515

0.104737708
0.104737708
0.145610364-0.1037218
0.003968579-0.003968579
0.013747194-0.013747194-0.019488836
0.019488836-0.061087863-0.244073033-0.022608896-0.022608896-0.015868124
0.036257754-0.041734307
0.041734307
0.074867923-0.074867923-0.033627011
0.033627011
0.006808842
0.063247878

meanLoudnessDSR

0.037596993

0.213296845

0.006873014-0.081620644

0.006917759

meanSharpness

0.210956438

0.02192087

0.005407844

0.024914351

0.026122222

meanSharpnessA

0.20979886-0.011959

0.009465615

0.033754659

0.046206691

meanSharpnessDS
R

0.206905402

0.02885395

0.012525552

0.015238963

0.032382536

・・・Fig 1. Overview of our research flow Participants: 30 students (15 musicians, 15 non-musicians ）- Musicians have at least 8 years of music experience (Taniguchi, 1989)

 Environment: Face to face in a sealed darkroom (Fig. 2)
 Stimuli: 20 stimuli (5 triads × 4 tones)- Triads: Major, Minor, Dim, Aug, Sus4 (Fig. 3)- Tones: Piano, Clarinet, Trumpet, Violin

Model Construction
Fig 9. Analysis Flow

 Evaluation- Affect Grid method (4 evaluation per 1 stimulus)- Rating scale method (1 evaluation per 1 stimulus)

Fig 2. Environment

 61 words
 7 scales Procedure- ❶Listening (4 sec) →❷Evaluation (Fig. 4 or Fig. 5)→❸Silence for 10 sec →❹Listening (4 sec)
Fig 3. Triads used for experimental stimuli(Repeat ❶～❹ for each stimuli)

Fig 4. Evaluation screen of Affect Grid method

Fig 5. Evaluation Screen of rating scale method

Result 1 (Type classification)- Divided into 3 types (Fig. 6)

 Weak positive: 8 people (3 musicians)

arousalFig 6. Distribution of correlation coefficients

arousalarousalarousal■：Major■：Minor■：Dim■：Aug■：Sus4■：Piano○：Clarinet●：Trumpet△：Violincorrelation coefficient: -0.286

Overallcorrelation coefficient: -0.749**

pleasureFig 7. Average rating per stimulus

Strong negativepleasure

correlation coefficient: -0.286

Weak negativepleasure

correlation coefficient: 0.317

Weak positive

Fig 8. Average rating per stimulus by type

Weak positiveStrong negative

Weak negative

Weak positiveFig 10. Construct by type

Result 1 (Type classification)- No association between musical experienceand tendency to evaluate affections- Representative value: Average of 4 evaluations

 Strong negative: 10 people (6 musicians)

Weak negative

Conclusion

Cluster analysis (Ward’s method)- Based on correlation coefficientsBetween pleasure and arousal

 Weak negative: 12 people (6 musicians)

Strong negativepleasure

Result 2 (Model construction)- Construct models by type
- 3 types of evaluation exist for the same soundFuture Study- Review the model construction- Validate the constructed modelValidate the estimation accuracyCompare with previous studies