  Most music emotion recognition approaches perform classification or
regression that estimates a general emotional category from a distribution of
music samples, but without considering emotional variations (e.g., happiness
can be further categorised into much, moderate or little happiness). We propose
an embedding-based music emotion recognition approach that associates music
samples with emotions in a common embedding space by considering both general
emotional categories and fine-grained discrimination within each category.
Since the association of music samples with emotions is uncertain due to
subjective human perceptions, we compute composite loss-based embeddings
obtained to maximise two statistical characteristics, one being the correlation
between music samples and emotions based on canonical correlation analysis, and
the other being a probabilistic similarity between a music sample and an
emotion with KL-divergence. The experiments on two benchmark datasets
demonstrate the effectiveness of our embedding-based approach, the composite
loss and learned acoustic features. In addition, detailed analysis shows that
our approach can accomplish robust bidirectional music emotion recognition that
not only identifies music samples matching with a specific emotion but also
detects emotions expressed in a certain music sample.
