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1 Expressive information 1. Emotions 2. Laban Effort space (gestures) 3. Kinestetic space (music performance) 4. Performance worm 5. Action based metaphor 1
2 Motivations " In human communication, two channels can be distinguished: " one transmits explicit messages, " the other transmits implicit messages about the humans themselves. " Explicit channel: " well studied " Implicit channel " not as well understood. " Understanding the other party emotions is one of the key tasks. " When a person engages with music, many mental process and contents are involved " representational and evaluative processes 2
3 How affect is differentiated from other psychological phenomena " Representational process: " e.g. determination or awareness of musical properties " as meter, rhythm, tonality, harmony, form, style " Central to recognition, identification, performance " Study of music perception and cognition " Evaluative process " e.g. determination or awareness of music as eliciting liking or disliking; preference; emotion and mood; aesthetic and spiritual experiences " are valenced: tendency to attach different degrees of positivity or negativity " are subjective: affected by labile personal factors as attitudes, associations, goals. " Key issue: understand the relationship between representational and evaluative process 3
4 1. Approaches to conceptualizing emotion " Aims of psychological approach: understanding " the mechanisms that intervene between " music reaching a person s ears and " an emotion being perceived, or experienced, " the roles of emotion in composing and performing music " Aim of the scientific and technological approach: to develop " models able to describe such phenomena " systems for expression and emotion rendering and recognizing in multimodal communication. " Approaches to conceptualizing emotion in psychology: " categorical " dimensional 4
5 1 a. Categorical approach " Assumption: people experience emotions as categories that are distinct from each other. " Basic emotions: " there is a limited number of innate and universal emotional categories, from which all other emotional states can be derived " happiness, sadness, anger, fear, " disgust, surprise " Focus: characteristics that distinguish emotion from one another " Critic: different set of basic emotions 5
6 1 b. Dimensional approach " Focus: identifying emotions based on their placement on a small number of dimensions, e.g. valence, activity, potency " Derived from similarity judgments analyzed using factor analysis or multidimensional scaling " Allow illustrating similarities between different feelings in terms of neighborhood in space 6
7 Circumplex model of Russel " Circular structure: valence and activation " Varying degree of similarity " Bipolar emotions " Organize in term of : " Valence: affect appraisal (pleasant unpleasant) " positive or negative evaluations of people or things or events " Activation: physiological reaction (high low arousal) " the strength of the person s disposition to take some action rather than none " Useful for capturing the continuous change in emotional expression " Critic: Blur important distinctions 7
8 Circumplex model of Russel " Valence " Activation (or Arousal) 8
9 Plutchik emotion wheel " Full-blown emotions tend to form a roughly circular pattern in activation-evaluation space. " è a circular structure inherent in emotionality. " Emotional orientation " angular measure " Emotional strength " distance from the origin " Strong emotions are more sharply distinct from each other than weaker emotions with the same emotional orientation. " Axes: " from acceptance to disgust " from apathetic to curious Anticipation Joy Anger Disgust Acceptance Fear Sadness Surprise The emotion wheel (Plutchik, 1980) 9
10 Typical characteristics of emotions " are functional despite their apparent non instrumentality " have behavioral, physiological and experiential components " have proximal elicitors " are intrinsically social " invoke action tendencies " change during the course of human development " involve cognitive appraisal of organism-environment relationships 10
11 2. Laban space: Expressive gestures Camurri - Volpe 11
12 Laban s Theory of Effort " In his Theory of Effort, choreographer Rudolf Laban points out " the dynamic nature of movement " the relationship among movement, space and time " Laban s approach is an attempt to " describe, in a formalized way, the major features of human movement " without focusing on a particular kind of movement or dance expression. " Gesture qualities described by the Effort space: " components: Space, Time, Weight, Flow => Extraction and analysis of Laban s gesture qualities is a step toward analysis and understanding of expressive gesture. 12
13 Laban s Space and Time components " Space component " refers to the " direction of a motion stroke and to the " path followed by a sequence of strokes (i.e., a sequence of directions). " flexible " if the movement follows these directions smoothly " direct " if it follows them along a straight trajectory. " Time component " is related to impulsiveness and capacity of controlling a movement. " With respect to Time an action can be sustained or quick. 13
14 Laban s Weight and Flow components " Weight component " is a measure of how much strength and weight is exerted in a movement. " It can be light or strong. " For example, in pushing away a heavy object it is necessary to use a strong weight, whereas in handling a delicate and light object, the weight component has to be light. " Flow component " is a measure of how bound or free a movement, or a sequence of movements, appears. 14
15 Laban s Theory of Effort 15
16 Laban s Theory of Effort 16
17 Example: Emotion and Motion " Movement affects emotional state and can produce stronger social connections. " How does/should this affect movement-based interaction? " Examining gestures and body movement in Wii games. " Linking the movements with emotional states and cues. " Used Laban Movement Analysis effort dimensions to better understand what makes certain movements feel certain ways. " happy games use light and sustained movements. " More directed, sudden movements up the competitive feel 17
18 3. Kinetics- Energy space: Expressive music performance Expressive music performance 18
19 3. Understanding expressiveness " Dimensional approach " Method " represent expressiveness on a low dimensional space " cluster analysis " dimensions interpretation " Dimensional representation: " asking subjects to evaluate according some scales " semantic effects " asking subjects to associate to specific attractors " focus on specific aspects " asking subjects to group by similarity " less semantic constraint 19
20 Perceptual analysis of expressive intentions " What? " Expressive music performances " Why? " Between performer s intention and listener experience " How? " Different scores " Different expressive (sensorial) intentions " Evaluation adjectives " Extract judgment categories (Cluster analysis, Factor analysis, MDS) " Result: " Expressive semantic space 20
21 3A. Kinetics Energy space " Aim of the perceptual analysis " determine the judgment categories used by the listeners. " Material " excerpts from Western Classical and ethnic music " different instruments " played according sensorial expressive intentions " hard, soft, light, heavy, bright, dark " neutral vs. natural " Subjects were asked to evaluate according 17 adjectives of sensorial nature " Sonological analysis of excerpts " Cluster analysis and factor analysis Canazza et al. JNMR
22 Methodology " A group of sensorial adjectives was chosen (hard, soft, light, heavy, bright, dark) which should inspire an expressive intention to a musician " Musicians (violin, clarinet, piano, sax) produced different performances of the same piece correlated to expressive intentions " Perceptual analyses confirmed that listener's experience and performer's intention were basically agreed " The sonological analyses shown the acoustic parameters which separate the different performances of the same score natural hard soft heavy light bright dark 22
23 Cluster Analysis " Cluster analysis shows clear distinction " group of trained musicians " group of non musicians (greater variance) Musicians Non musicians distance index between the subjects 23
24 Perceptual analysis " Factor analysis on evaluation adjectives: " black, oppressive, serious, dismal, massive, rigid, mellow, tender, sweet, limpid, airy, gentle, effervescent, vaporous, fresh, abrupt, sharp " The performances are placed on it, according to factor scores, in order to observe in which semantic sector they map " Performances placed near similar adjectives " Neutral in the middle 24
25 Kinetics- Energy space " Factor analysis on performances: 2 factors " Interpretation " dim. 1 (Heavy-Light) è Kinetics " dim. 2 (Hard-Soft) è Energy 1,0 tempo legato intensity Hard factor factor ,5 Heavy Dark " Abstract control space Energy 0,0-0,5 Light Bright Soft -1,0-1,0-0,5 0,0 0,5 1,0 Kinetics 25
26 Factor analysis on performances " Subjects placed the performances along only two axes " Factor 1 distinguishes Heavy from Light " Factor 2 distinguishes Soft from Hard " Indication of how listeners organized the performances in their own minds. 1,0 Hard 0,5 Heavy Dark Energy 0,0-0,5 Light Bright -1,0 Soft -1,0-0,5 0,0 0,5 1,0 Kinetics 26
27 Interpretation: Kinetics-Energy space " X-axis (Heavy-Light) " correlated with Tempo " è Kinetics " Y-axis (Hard-Soft) " correlated with Attack Time, Legato/Staccato, Intensity " è Energy " Kinetics - Energy define an abstract representation space " è Kinestetic space Energy 1,0 0,5 0,0-0,5-1,0 Heavy Dark Hard Soft Light Bright -1,0-0,5 0,0 0,5 1,0 Kinetics 27
28 4. Performance worm: Expressive music performance Expressive music performance 28
29 Performance Worm (Dixon 2002) " Skilled musicians communicate high level information such as musical structure and emotion when they shape the music by the continuous modulation of aspects such as tempo and loudness. " The Performance Worm is a real time system for tracking and visualizing the tempo and dynamics of a performance " provides insight into the expressive patterns applied by skilled artists. " This representation also forms the basis for automatic recognition of performers style 29
30 Performance worm " horizontal axis è tempo in beats per minute, " vertical axis è loudness in sones. Rachmaninov s Prelude op.23 no.6 played by Vladimir Ashkenazy, bar
31 To learn more " Cowie R., Douglas-Cowie E., Tsapatsoulis N., Votsis G., Kollias S., Fellenz W., Taylor J., Emotion Recognition in Human-Computer Interaction, IEEE Signal Processing Magazine, vol. 18, no. 1, pp , " Sloboda J.A. and Juslin P. (eds), Music and Emotions, Oxford University Press, 2001 " Laban R. and Lawrence F.C., Effort. London: Macdonald & Evans Ltd., " Canazza S., De Poli G., Rodà A. and Vidolin A., An Abstract Control Space for Communication of Sensory Expressive Intentions in Music Performance Journal of New Music Research, vol. 32, n. 3, pp , 2003 " Dixon, S., Goebl, W., and Widmer, G. (2002). Real time tracking and visualisation of musical expression. in Music and Artificial Intelligence: Second International Conference, ICMAI2002, pages 58 68, Edinburgh, Scotland. Springer. 31
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