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A neural network model for the prediction of musical emotions

A neural network model for the prediction of musical emotions

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This chapter presents a novel methodology to analyse the dynamics of emotional responses to music in terms of computational representations of perceptual processes (psychoacoustic features) and self-perception of physiological activation (peripheral feedback). The approach consists of a computational investigation of musical emotions based on spatio-temporal neural networks sensitive to structural aspects of music. We present two computational studies based on connectionist network models that predict human subjective feelings of emotion. The first study uses six basic psychoacoustic dimensions extracted from the music pieces as predictors of the emotional response. The second computational study evaluates the additional contribution of physiological arousal to the subjective feeling of emotion. Both studies are backed up by experimental data. A detailed analysis of the simulation models' results demonstrates that a significant part of the listener's affective response can be predicted from a set of psychoacoustic features of sound tempo, loudness, multiplicity (texture), power spectrum centroid (mean pitch), sharpness (timbre) and mean STFT flux (pitch variation) and one physiological cue, heart rate. This work provides a new methodology to the field of music and emotion research based on combinations of computational and experimental work, which aid the analysis of emotional responses to music, while offering a platform for the abstract representation of those complex relationships.

Inspec keywords: physiology; musical acoustics; psychology; neural nets; bioacoustics; music; acoustic signal processing; hearing

Other keywords: perceptual process; mean STFT flux; sound loudness; physiological cue; physiological activation; timbre; spatio-temporal neural network; listener affective response; connectionist network model; physiological arousal; neural network model; mean pitch; music piece; sound tempo; emotional response; pitch variation; musical emotion prediction; music structural aspect; human subjective feeling; heart rate; dynamic analysis; peripheral feedback; psychoacoustic feature; power spectrum centroid; psychoacoustic dimension; computational representation; self-perception; sound sharpness

Subjects: Audition

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