Abstract
An approach is proposed to estimate the emotional probability distribution of a novel music emotion model based on the updated Hevner’s 8 emotion groups. Possible application includes browsing and mixing music with different emotion distributions. It is based on ground truths collected for 200 30-s clips (subdivided into 1200 segments further) chosen from soundtrack and labeled by 328 subjects online. Averagely, there are 28.2 valid emotional labeling events per clip, and constructing a probability distribution. Next, 88 musical features were extracted by 4 existing programs. The most discriminative 29 features were selected out by the pair-wise F-score comparison. The resultant 1200 segments were randomly separated into 600 training and 600 testing data, and input to SVM to estimate an 8-class probability distribution. They are finally evaluated by cosine, intersection, and quadratic similarity with the ground truth, where the quadratic metric achieves the best 87.3% ± 12.3% similarity.
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Wu, TL., Jeng, SK. (2008). Probabilistic Estimation of a Novel Music Emotion Model. In: Satoh, S., Nack, F., Etoh, M. (eds) Advances in Multimedia Modeling. MMM 2008. Lecture Notes in Computer Science, vol 4903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77409-9_46
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DOI: https://doi.org/10.1007/978-3-540-77409-9_46
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