Topics in Cognitive Science 4 (4):607-624 (2012)

Abstract
An overview of the computational prediction of emotional responses to music is presented. Communication of emotions by music has received a great deal of attention during the last years and a large number of empirical studies have described the role of individual features (tempo, mode, articulation, timbre) in predicting the emotions suggested or invoked by the music. However, unlike the present work, relatively few studies have attempted to model continua of expressed emotions using a variety of musical features from audio-based representations in a correlation design. The construction of the computational model is divided into four separate phases, with a different focus for evaluation. These phases include the theoretical selection of relevant features, empirical assessment of feature validity, actual feature selection, and overall evaluation of the model. Existing research on music and emotions and extraction of musical features is reviewed in terms of these criteria. Examples drawn from recent studies of emotions within the context of film soundtracks are used to demonstrate each phase in the construction of the model. These models are able to explain the dominant part of the listeners’ self-reports of the emotions expressed by music and the models show potential to generalize over different genres within Western music. Possible applications of the computational models of emotions are discussed
Keywords Emotion  Acoustic analysis  Dimensions  Feature extraction  Affect  Computational model
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DOI 10.1111/j.1756-8765.2012.01188.x
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References found in this work BETA

Sweet Anticipation: Music and the Psychology of Expectation.William Benjamin - 2007 - Journal of Aesthetics and Art Criticism 65 (3):333-335.

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Citations of this work BETA

Musical Pluralism and the Science of Music.Adrian Currie & Anton Killin - 2016 - European Journal for Philosophy of Science 6 (1):9-30.
Music Cognition and the Cognitive Sciences.Marcus Pearce & Martin Rohrmeier - 2012 - Topics in Cognitive Science 4 (4):468-484.

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