Multi-Modal Non-Prototypical Music Mood Analysis in Continuous Space: Reliability and Performances

Music Mood Classification is frequently turned into ‘Music Mood Regression’ by using a continuous dimensional model rather than discrete mood classes. In this paper we report on automatic analysis of performances in a mood space spanned by arousal and valence on the 2.6 k songs NTWICM corpus of popular UK chart music in full realism, i. e., by automatic web-based retrieval of lyrics and diverse acoustic features without pre-selection of prototypical cases. We discuss optimal modeling of the gold standard by introducing the evaluator weighted estimator principle, group-wise feature relevance, ‘tuning’ of the regressor, and compare early and late fusion strategies. In the result, correlation coefficients of .736 (valence) and .601 (arousal) are reached on previously unseen test data.

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