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What is Music Dimensional Models

Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence
consider that perceptual, cognitive or emotional states associated with music listening can be defined by a position in a continuous multidimensional space where each dimension stands for a fundamental property common to all the observed states. Pitch, for example, is considered to be defined by a height (how high or low in pitch it is a tone) and a chroma (the note class it belongs to, i.e., C, D, E, etc.) dimension. Two of the most accepted dimensions for describing emotions were proposed by Russel (Russel 1980): valence (positive versus negative affect) and arousal (low versus high level of activation). This variety of dimensions could be seen as the different expressions of a very small set of basic concepts.
Published in Chapter:
Automatic Detection of Emotion in Music: Interaction with Emotionally Sensitive Machines
Cyril Laurier (Universitat Pompeu Fabra, Spain) and Perfecto Herrera (Universitat Pompeu Fabra, Spain)
DOI: 10.4018/978-1-60566-354-8.ch002
Abstract
Creating emotionally sensitive machines will significantly enhance the interaction between humans and machines. In this chapter we focus on enabling this ability for music. Music is extremely powerful to induce emotions. If machines can somehow apprehend emotions in music, it gives them a relevant competence to communicate with humans. In this chapter we review the theories of music and emotions. We detail different representations of musical emotions from the literature, together with related musical features. Then, we focus on techniques to detect the emotion in music from audio content. As a proof of concept, we detail a machine learning method to build such a system. We also review the current state of the art results, provide evaluations and give some insights into the possible applications and future trends of these techniques.
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