  Emotion is a complicated notion present in music that is hard to capture even
with fine-tuned feature engineering. In this paper, we investigate the utility
of state-of-the-art pre-trained deep audio embedding methods to be used in the
Music Emotion Recognition (MER) task. Deep audio embedding methods allow us to
efficiently capture the high dimensional features into a compact
representation. We implement several multi-class classifiers with deep audio
embeddings to predict emotion semantics in music. We investigate the
effectiveness of L3-Net and VGGish deep audio embedding methods for music
emotion inference over four music datasets. The experiments with several
classifiers on the task show that the deep audio embedding solutions can
improve the performances of the previous baseline MER models. We conclude that
deep audio embeddings represent musical emotion semantics for the MER task
without expert human engineering.
