  Symbolic Music Emotion Recognition(SMER) is to predict music emotion from
symbolic data, such as MIDI and MusicXML. Previous work mainly focused on
learning better representation via (mask) language model pre-training but
ignored the intrinsic structure of the music, which is extremely important to
the emotional expression of music. In this paper, we present a simple
multi-task framework for SMER, which incorporates the emotion recognition task
with other emotion-related auxiliary tasks derived from the intrinsic structure
of the music. The results show that our multi-task framework can be adapted to
different models. Moreover, the labels of auxiliary tasks are easy to be
obtained, which means our multi-task methods do not require manually annotated
labels other than emotion. Conducting on two publicly available datasets
(EMOPIA and VGMIDI), the experiments show that our methods perform better in
SMER task. Specifically, accuracy has been increased by 4.17 absolute point to
67.58 in EMOPIA dataset, and 1.97 absolute point to 55.85 in VGMIDI dataset.
Ablation studies also show the effectiveness of multi-task methods designed in
this paper.
