Combining Language Modeling and LSA on Greek Song “Words” for Mood Classification

Abstract

The present work presents a novel approach to song mood classification. Two language models, one absolute and one relative, are experimented with. Two distinct audio feature sets are compared against each other, and the significance of the inclusion of text stylistic features is established. Furthermore, Latent Semantic Analysis is innovatively combined with language modeling, and depicts the discriminative power of the latter. Finally, song “words” are defined in a broader sense that includes lyrics words as well as audio words, and LSA is applied to this augmented vocabulary with highly promising results. The methodology is applied to Greek songs, that are classified into one of four valence and into one of four arousal categories.