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Analysis of Emotional Dimensions in Music Using Time Series Techniques Analysis of Emotional Dimensions in Music Using Time Series Techniques Emery Schubert Few who have experienced the thrill of emotional interactions with music would deny that such experiences are important and enriching. These intoxicating episodes can be superior to any heightening or soothing drug because they occur without unpleasant side-effects. The fact that music can express such emotions—move people to tears of lament and to teeth-clenching ecstasy—is, for many, a wondrous mystery. How is it that by manipulating combinations of innocuous acoustic parameters composers and performers can produce such potent effects? The question has occupied scholars and practitioners for over twenty centuries. One of the many problems that face researchers is the lack of objective data available for comparing music and emotion. Further, experimental research in this area has tended to treat music as a stimulus that can be dealt with by a single point response. While experimental methodology requires an element of reductionism, in this article I argue that it is possible to obtain an additional dimension to the understanding of emotion in music by tracking emotional responses to music continuously. My work is based on the fundamental assumption that there are underlying rules that govern the relationship between music and emotion within a given culture.1 The strength of the experimental approach has been summarised by Pike, who indicated the benefit of collecting responses from several people instead of the introspections of the 1 My approach implies both a reductionist and behaviourist approach. While the approach of the reductionist is present, I rebuff the claim of a behaviourist approach. Behaviourists study input and output and their correlation and then contend that nothing else is worthy of study. See B.F. Skinner, About Behaviorism (Oxford: Alfred A. Knopf, 1974). Those who believe that this correlation is interesting (including this author) must still study input (music), output (emotion) and their correlations. In other words, I support the notion that there is a necessary cognitive mediation between music and emotion. As John Sloboda put it: ‘Seen with the cold eye of physics a musical event is just a collection of sounds with various pitches, durations, and other measurable qualities. Somehow the human mind endows these sounds with significance. They become symbols for something other than pure sound, something which enables us to laugh or cry, like or dislike, be moved or be indifferent.’ J.A. Sloboda, The Musical Mind: The Cognitive Psychology of Music (London: OUP, 1985) 1. 65 66 Context 31 lone expert.2 However, the empirical approach, stemming from the turn of the century, was often hindered by methodological flaws and technological deficiencies. Such studies provided fuel for the formalists and expressionists who believed in the strongly subjective nature of musical experience.3 For example, Heinlein found that the widely accepted relationship between happy–major and sad–minor was without foundation, but some fifty years later the same data were reanalysed by Crowder and found to be misinterpreted.4 In the meantime, several empirical investigations upheld the thesis of a relationship between emotions and musical features, even though this view received no serious attention from introspectionists.5 In his influential book, Emotion and Meaning in Music, Meyer referred to the Heinlein study,6 but not to the work of Hevner, Gundlach, Sherman, Rigg and several others who, in the 1920s and ’30s, had found more positive relationships between specific emotions and musical features.7 The methodological problems that faced empirical researchers were numerous. ‘Real’ pieces of music usually have a large number of interacting musical features making the investigation of possible causal connections between musical features and emotional response more difficult to unravel. Further, some researchers required listeners to select a word from a list to describe the emotion in a piece of music, even though the piece typically expressed more than one emotion. This methodology made it impossible to distinguish whether the listener was making an overall reflection about the piece or responding to a salient part of the music. One solution was to select isolated samples of sound so that a single musical structure or feature could be manipulated and tested for emotional expressivity. Such research is often criticised because it is too far removed from naturalistic music. While this may be true, it was one of the few practical ways of investigating the problem, and it could supply convergent evidence, or otherwise, with investigations that used ‘real’ music. Until quite recently empirical researchers had to contend with static, asynchronous measurements of the dynamic process of music and emotional response. It is easy to see why this type of approach can be criticised: how could measured responses made at the end of a piece possibly cover the subtle and abrupt changes which music expresses during a performance? However, advances in computer technology now facilitate improvements in methodology that were not available to these early pioneers. Modern technology offers new opportunities 2 A. Pike, ‘A Phenomenological Analysis of Emotional Experience in Music,’ Journal of Research in Music Education 20 (1972): 262–68. 3 L.B. Meyer, Emotion and Meaning in Music (Chicago: U of Chicago Press, 1956); B. Reimer, A Philosophy of Music Education (Englewood Cliffs, NJ: Prentice Hall, 1989). 4 C.P. Heinlein, ‘The Affective Characters of the Major and Minor Modes in Music,’ Journal of Comparative Psychology 8 (1928): 101–42 and R.G. Crowder, ‘Perception of the Major/Minor Distinction: I. Historical and Theoretical Foundations,’ Psychomusicology 4.1–2 (1984): 3–12. 5 Meyer, Emotion and Meaning in Music; S.K. Langer, Philosophy in a New Key: A Study in the Symbolism of Reason, Rite, and Art (Cambridge, MA: Harvard UP, 1957). 6 Heinlein, ‘Affective Characters.’ 7 K. Hevner, ‘The Affective Character of the Major and Minor Modes in Music,’ American Journal of Psychology 47 (1935): 103–18; K. Hevner, ‘Experimental Studies of the Elements of Expression in Music,’ American Journal of Psychology 48 (1936): 246–68; R.H. Gundlach, ‘Factors Determining the Characterization of Musical Phrases,’ American Journal of Psychology 47 (1935): 624–43; M. Sherman, ‘Emotional Character of the Singing Voice,’ Journal of Experimental Psychology 11 (1928): 495–97; M. Rigg, ‘An Experiment to Determine how Accurately College Students can Interpret the Intended Meanings of Musical Compositions,’ Journal of Experimental Psychology 21 (1937): 223–29. Emotional Dimensions in Music 67 to develop methods of obtaining responses often enough during a performance to capture the dynamic flow of expressions in the music. Measurement of continuous response to music is a growing area of research, but such research has not been supported with quantitative analyses. Many previous music-emotion studies fail to use analytic techniques for relating continuous responses with the components of the continuous stimulus. Sophisticated time series analytic techniques have been available in the fields of statistics, geology and econometrics for more than thirty years, yet these procedures are only starting to spread to the study of emotion in music. I have described my position with regard to the relationship between emotion and music: I believe that there is a relationship between emotion and music, and further, that there is an underlying, quantifiable relationship between musical features and emotional response. Before describing how this information can be modelled using time series analysis it is necessary to define the structure of emotion so that we can establish a means for quantifying it. The Structure of Emotion There are two broad systems of classifying emotions: categories and dimensions. Categorical classification of emotion assumes that emotions carrying different meanings, such as happy and sad, are distinct and independent entities. Checklists imply a categorical classification of emotions. For example, Tomkins’s research on facial expressions suggested that emotions could be grouped into one of eight categories: 8 1. interest/excitement 5. fear/terror 2. enjoyment/joy 6. shame/humiliation 3. surprise/startle 7. contempt/disgust 4. distress/anguish 8. anger/rage. In contrast, the dimensional classification of emotion holds that all emotions are in some way related within an n-dimensional semantic space (or, more correctly, emotion space). For example, the dimensional structure suggests that happy and sad are opposite emotions along the valence dimension of emotion. To distinguish distinct emotions having similar valence, such as sad and angry, a second dimension, arousal (or activity), may be added: sad (typically) has low arousal and angry has high arousal. Dimensional classification can help to visualise the interrelationships between emotions and to provide a structured framework for emotion research. Several prominent psychologists support the use of the dimensional approach for the study of emotions.9 However, opinions on 8 S.S. Tomkins, Affect, Imagery, Consciousness (New York: Springer, 1962); S.S. Tomkins and C.E. Izard, eds, Affect, Cognition, and Personality: Empirical Studies (New York: Springer, 1965). 9 H. Schlosberg, ‘Three Dimensions of Emotion,’ Psychological Review 61 (1954): 81–88; R. Plutchik, The Emotions: Facts, Theories and a New Model (New York: Random House, 1962);
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