Affective Feature Design and Predicting Continuous Affective Dimensions from Music

Abstract

This paper presents affective features designed for music and develops a method to predict dynamic emotion ratings along the arousal and valence dimensions. We learn a model to predict continuous time emotion ratings based on combination of global and local features. This allows us to exploit information from both the scales to make a more robust prediction.

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