Multimodal music emotion classification using AdaBoost with decision stumps

We propose using AdaBoost with decision stumps to implement multimodal music emotion classification (MEC) as a more appropriate alternative to the conventional SVMs. By modeling the presence or absence of salient phrases in the lyric texts and seeking for proper thresholds for certain audio signal features, it exploits interdependencies between aspects from both modalities in the multimodal MEC system to make the final classification. It can especially prevent the “short text problem” in lyrics. Our accuracy reached an average of 78.19% for classifying 3766 unique songs into 14 emotion categories, with a statistically significant improvement over the audio-only and lyrics-only monomodal MEC systems. We also show that the proposed AdaBoost with decision stumps method performs statistically better on multimodal MEC than the well-known SVM classifier, which only has an average accuracy of 72.08%.